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<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Explor Digit Health Technol</journal-id>
<journal-id journal-id-type="publisher-id">EDHT</journal-id>
<journal-title-group>
<journal-title>Exploration of Digital Health Technologies</journal-title>
</journal-title-group>
<issn pub-type="epub">2996-9409</issn>
<publisher>
<publisher-name>Open Exploration Publishing</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.37349/edht.2024.00026</article-id>
<article-id pub-id-type="manuscript">101126</article-id>
<article-categories>
<subj-group>
<subject>Review</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Harnessing the potential of ChatGPT in pharmacy management: a concise review</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-7627-4970</contrib-id>
<name>
<surname>Noman</surname>
<given-names>Abdullah Al</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing—original draft</role>
<xref ref-type="aff" rid="I1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1">
<sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Fahim</surname>
<given-names>MD Ismail Ahmed</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing—review &amp; editing</role>
<xref ref-type="aff" rid="I2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tonny</surname>
<given-names>Tamanna Shahrin</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing—original draft</role>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<xref ref-type="aff" rid="I1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Samia</surname>
<given-names>Afroza Akter</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/validation/">Validation</role>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing—review &amp; editing</role>
<role content-type="https://credit.niso.org/contributor-roles/software/">Software</role>
<xref ref-type="aff" rid="I1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5870-7653</contrib-id>
<name>
<surname>Moinuddin</surname>
<given-names>Sakib M.</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing—review &amp; editing</role>
<xref ref-type="aff" rid="I3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="editor">
<name>
<surname>Yeung</surname>
<given-names>Andy Wai Kan</given-names>
</name>
<role>Academic Editor</role>
<aff>The University of Hong Kong, China</aff>
</contrib>
</contrib-group>
<aff id="I1">
<sup>1</sup>School of Pharmacy, BRAC University, Dhaka 1212, Bangladesh</aff>
<aff id="I2">
<sup>2</sup>Department of Computer Science &amp; Technology, State University of Bangladesh, Dhaka 1461, Bangladesh</aff>
<aff id="I3">
<sup>3</sup>Department of Pharmaceutical and Biomedical Sciences, California Northstate University, Elk Grove, CA 95757, USA</aff>
<author-notes>
<corresp id="cor1">
<bold>*Correspondence:</bold> Abdullah Al Noman, School of Pharmacy, BRAC University, Dhaka 1212, Bangladesh, <email>abdullah.al.noman@g.bracu.ac.bd</email></corresp>
</author-notes>
<pub-date pub-type="ppub">
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>18</day>
<month>09</month>
<year>2024</year>
</pub-date>
<volume>2</volume>
<issue>5</issue>
<fpage>259</fpage>
<lpage>270</lpage>
<history>
<date date-type="received">
<day>11</day>
<month>05</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>14</day>
<month>08</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>© The Author(s) 2024.</copyright-statement>
<license xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.</license-p>
</license>
</permissions>
<abstract>
<p id="absp-1">ChatGPT is one of the promising AI-based language models which has the potential to contribute to pharmacy settings in many aspects. This paper focuses on the possible aspects of pharmacy management where ChatGPT can contribute, the prevalence of its use in Saudi Arabia as a practical insight, case studies showing the potential of ChatGPT in answering health-related enquiries, its benefits, challenges, and future prospects of it. Helping clients, verifying medication, examining for potential reactions to drugs, identifying potential interaction between drugs, providing recommendation for suitable alternative medication therapies, assisting healthcare workers and supporting the search for novel medication are the biggest roles that are cited. The study highlights several benefits of using ChatGPT, including greater medical supervision, fewer drug errors, greater power over existing equipment, and support to study about the medicine sector. However, concerns about security, reliability, privacy, over-reliance on AI, and lack of natural judgement must be addressed by careful implementation under human review. The study also provided insight of practical application of ChatGPT in pharmacy education and possible ways of implementing ChatGPT in getting improved care and optimized operation. The future prospect of ChatGPT is promising but requires increased precision, integration of it into education programs, progressing of patient treatment and interaction, and facilitating novel research abilities. In general, the review suggests that ChatGPT has the potential to improve and modernize pharmacy processes but cautious implementation of this developing AI technology, combined with human knowledge is important to improve healthcare in the pharmaceutical field.</p>
</abstract>
<kwd-group>
<kwd>ChatGPT</kwd>
<kwd>pharmacy practice</kwd>
<kwd>artificial intelligence</kwd>
<kwd>workflow automation</kwd>
<kwd>patient counseling</kwd>
<kwd>medication safety</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p id="p-1">The use, expense, and range of pharmacy research are expanding. Innovative management strategies are required due to the distinctive character of various disorders managed by specialized medications, such as cancer and rheumatoid arthritis [<xref ref-type="bibr" rid="B1">1</xref>]. Pharmacies and chemists are only one example of the several healthcare professions that exist to cure sickness and encourage better health. Although the main objective is to enhance patient medical results and standard of daily life, drugstores remain active businesses and need to be handled effectively in order to keep their clientele, provide therapeutic services, be successful, and develop over time [<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>].</p>
<p id="p-2">A natural language processing (NLP) system called ChatGPT was created by OpenAI. By comprehending the context of a discussion and producing suitable responses, it is intended to produce dialogues that are human-like. A deep learning model named GPT-3, which was trained on a sizable dataset of chats, forms the foundation of ChatGPT [<xref ref-type="bibr" rid="B4">4</xref>]. By giving the helpers greater and more precise assistance, ChatGPT may be used to raise the quality of the preceding services. These assistants can offer individualized suggestions and counsel, such as meal plans or medication reminders, to aid patients in managing their medical problems [<xref ref-type="bibr" rid="B5">5</xref>]. Hariri [<xref ref-type="bibr" rid="B5">5</xref>] and Zhai [<xref ref-type="bibr" rid="B6">6</xref>] conducted that intelligent tutoring systems that can give students individualized learning experiences have been developed using ChatGPT. These systems are able to recognize the various learning preferences of students and modify the lesson plan and instructional techniques to better suit their requirements [<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B7">7</xref>]. Additionally, chatbots that can respond to consumer questions and assistance requests have been created using ChatGPT. These chatbots can comprehend text written in natural language and offer tailored replies, enhancing the general customer experience and lightening the burden of customer support representatives. High-quality content for websites, social media platforms, and advertising campaigns has been produced using ChatGPT. The ability of translating text between languages has been created using ChatGPT and delivering translations that are appropriate for the situation helps individuals from various cultures and backgrounds communicate more effectively. Engineers in the field of computing and programming aficionados can also benefit from ChatGPT [<xref ref-type="bibr" rid="B5">5</xref>].</p>
<p id="p-3">By producing simple descriptions of pharmacology, pharmacokinetics, and interactions among drugs, offering examples and case studies, creating slides and assessments, and referring to pertinent resources, ChatGPT can help with pharmacy education. By creating virtual patient contacts, offering feedback on interaction and counselling approaches, and producing natural language replies to frequent patient inquiries and circumstances, ChatGPT can help with the growth of patient counselling and drug handling abilities (<xref ref-type="fig" rid="fig1">Figure 1</xref>). The dangers to confidentiality and safety, the reliability and precision of created material, the potential for dependence on science and technology, the inability to recognize bias or mistakes, and the potential for diminished imagination and critical thinking are some issues and difficulties that could arise from using ChatGPT in the field of pharmacy. It is critical to incorporate human supervision and input, set ethical standards for employment, guarantee security and privacy measures have been put place, and routinely check for any discrimination or mistakes in order to solve ChatGPT’s shortcomings in educating pharmacists and maintain safeguards for patients and privacy [<xref ref-type="bibr" rid="B8">8</xref>]. This concise review shows the probable applications, shortcomings, and use cases of ChatGPT management along with the probable future of the technology in the future.</p>
<fig id="fig1" position="float">
<label>Figure 1</label>
<caption>
<p id="fig1-p-1">ChatGPT potential in pharmacy management. Icons made by <ext-link xlink:href="https://www.flaticon.com/authors/freepik" ext-link-type="uri">Freepik</ext-link> from <ext-link xlink:href="http://www.flaticon.com/" ext-link-type="uri">www.flaticon.com</ext-link></p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="edht-02-101126-g001.tif" />
</fig>
</sec>
<sec id="s2">
<title>Methodology</title>
<sec id="t2-1">
<title>Search design and data source</title>
<p id="p-4">The search strategy for this review was designed to capture relevant literature on the application of ChatGPT and similar large language models in pharmacy management. Primary searches were conducted in PubMed, Scopus, and Web of Science databases, using a combination of MeSH terms and keywords related to artificial intelligence (AI), language models, and pharmacy practice. The search string included: (“ChatGPT” OR “GPT-3” OR “large language model*” OR “artificial intelligence”) AND (“pharmacy management” OR “pharmacy administration” OR “pharmacy practice” OR “pharmaceutical services”). To ensure comprehensive coverage, additional sources were consulted, including Google Scholar for gray literature, and relevant conference proceedings from major pharmacy and health informatics conferences.</p>
</sec>
<sec id="t2-2">
<title>Inclusion and exclusion criteria</title>
<p id="p-5">To ensure the relevance and quality of the included studies, specific inclusion and exclusion criteria were established. Studies were included if they (<xref ref-type="table" rid="t1">Table 1</xref>): (1) focused on the application of ChatGPT or similar large language models in pharmacy management or practice; (2) were published in English between 2020 and present; (3) were original research articles, systematic reviews, meta-analyses, or case studies; and (4) reported on outcomes such as efficiency, cost-effectiveness, error reduction, or patient satisfaction in pharmacy settings. Exclusion criteria encompassed (<xref ref-type="table" rid="t1">Table 1</xref>): (1) studies not specifically related to pharmacy or ChatGPT; (2) opinion pieces, editorials, or letters to the editor; (3) publications focusing on AI technologies other than large language models; (4) studies conducted in non-pharmacy healthcare settings; and (5) purely theoretical discussions without clear outcomes. Additionally, only peer-reviewed publications with full-text availability were considered. This careful selection process aimed to capture the most relevant and high-quality evidence on the potential of ChatGPT in pharmacy management while excluding tangential or less rigorous sources.</p>
<table-wrap id="t1">
<label>Table 1</label>
<caption>
<p id="t1-p-1">Inclusion and exclusion criteria</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Criteria</th>
<th>Inclusion</th>
<th>Exclusion</th>
</tr>
</thead>
<tbody>
<tr>
<td>Publication type</td>
<td>Original research articles, systematic reviews, meta-analyses, case studies</td>
<td>Opinion pieces, editorials, letters to the editor</td>
</tr>
<tr>
<td>Language</td>
<td>English</td>
<td>Non-English publications</td>
</tr>
<tr>
<td>Publication date</td>
<td>2020–present</td>
<td>Publications before 2020</td>
</tr>
<tr>
<td>Study focus</td>
<td>Applications of ChatGPT or similar AI in pharmacy management</td>
<td>AI applications not specific to pharmacy or ChatGPT not mentioned</td>
</tr>
<tr>
<td>Setting</td>
<td>Pharmacy settings (community, hospital, clinical)</td>
<td>Non-pharmacy healthcare settings</td>
</tr>
<tr>
<td>AI technology</td>
<td>ChatGPT, GPT-3, GPT-4, or similar large language models</td>
<td>Other AI technologies not related to language models</td>
</tr>
<tr>
<td>Outcomes</td>
<td>Efficiency, cost-effectiveness, error reduction, patient satisfaction</td>
<td>Studies without clear outcomes or purely theoretical discussions</td>
</tr>
<tr>
<td>Accessibility</td>
<td>Full-text available</td>
<td>Abstract only</td>
</tr>
<tr>
<td>Peer review</td>
<td>Peer-reviewed publications</td>
<td>Non-peer-reviewed sources</td>
</tr>
<tr>
<td>Sample size</td>
<td>N/A (dependent on study design)</td>
<td>Case reports with single subject</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s3">
<title>Applications of ChatGPT in pharmacy</title>
<p id="p-6">By interpreting natural language rapidly and effectively, ChatGPT can improve healthcare duties including helping patients and medical record maintenance. By reducing manual entry of data into computerized medical records, it can speed up interaction among doctors and nurses, increase reliability, and reduce time. With the use of ChatGPT technology, patients could get one-on-one assistance from simulated assistants, which improves their experience by cutting down on the period it takes for doctors to respond and giving them additional time to handle complicated situations [<xref ref-type="bibr" rid="B9">9</xref>].</p>
<p id="p-7">The potential of using ChatGPT-4 language model-based AI to provide younger doctors with medical guidance in medical situations. The research found that ChatGPT-4 performed exceptionally well at giving accurate and logical medical counsel. These researchers think that AI models like ChatGPT-4 might alter the medical curriculum and help to ease the limitations on clinical exposure. They do acknowledge that ChatGPT-4 has limitations when interacting with extremely particular industries by developing genuine patients case studies, increasing medical educational materials with clarification, examples, and illustrations [<xref ref-type="bibr" rid="B10">10</xref>]. Also analyzing medical literature publications, language models can improve medical students testing and the capacity to solve problems while utilizing time as well as keeping them up updated by the most recent findings in their area of expertise [<xref ref-type="bibr" rid="B11">11</xref>].</p>
<p id="p-8">AI has shown promising results in promoting drug adherence and monitoring faithfulness. However, evaluating adherence to medicines remains challenging. Common strategies include self-reporting, digital regulations, and other qualitative and quantitative methods. However, there is a weak-to-moderate association between self-reported measurements and computerized measurements and medication refills. Machine learning methods, particularly in machine learning, could improve the trustworthiness and accuracy of adherence metrics [<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>]. AI can aid integrated medical systems, data exchange, and patient self-care through multidisciplinary studies systems, computational medical graphs, and electronic medical records (<xref ref-type="table" rid="t2">Table 2</xref>). It can also help medical information managers organize and harvest information from unorganized electronic medical documents using NLP and computational AI techniques. AI-assisted innovation can optimize instructions, prioritize prescriptions, and reduce pharmaceutical errors, such as pharmaceutical reconciliation, which is often used to reduce mistakes [<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>].</p>
<table-wrap id="t2">
<label>Table 2</label>
<caption>
<p id="t2-p-1">Summary of some of the possible applications of ChatGPT in pharmacy</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Application</th>
<th>Description</th>
<th>Example</th>
<th>References</th>
</tr>
</thead>
<tbody>
<tr>
<td>Customer support</td>
<td>ChatGPT can answer common queries from customers about their medications, such as dosage, side effects, availability, etc.</td>
<td>Customer: How often should I take this antibiotic? ChatGPT: You should take one tablet every 12 hours for 7 days.</td>
<td>[<xref ref-type="bibr" rid="B20">20</xref>]</td>
</tr>
<tr>
<td>Drug interaction checks</td>
<td>ChatGPT can analyze the medications that a patient is taking and alert the pharmacist to any potential risks of adverse drug reactions.</td>
<td>Pharmacist: What other medications are you taking? Patient: I’m taking aspirin for my headache. ChatGPT: Aspirin may interact with your antibiotic and increase the risk of bleeding. You should consult your doctor before taking aspirin.</td>
<td>[<xref ref-type="bibr" rid="B15">15</xref>]</td>
</tr>
<tr>
<td>Prescription validation</td>
<td>ChatGPT can verify the accuracy and legality of prescriptions, identifying any errors or inconsistencies.</td>
<td>Prescription: Amoxicillin 500 mg, 1 tablet twice daily for 10 days. ChatGPT: This prescription is valid and matches the standard dosage for amoxicillin.</td>
<td>[<xref ref-type="bibr" rid="B21">21</xref>]</td>
</tr>
</tbody>
</table>
</table-wrap>
<p id="p-9">ChatGPT, an AI-based technology, can predict and clarify medication interactions, aiding those without immediate healthcare communication. However, it may not provide comprehensive assistance, and further work is needed to improve its accuracy and enhance its utility for patients. A study assessed ChatGPT’s ability to predict and explain drug-drug interactions (DDIs), revealing that 39 out of 40 cases had valid solutions. However, half of the scenarios had unclear solutions. The study used pharmaceutical perspectives to assess the precision, with a layman’s perspective stating that the data is accurate and can help patients identify DDIs [<xref ref-type="bibr" rid="B15">15</xref>]. The ChatGPT-4 application highlights the potential to improve patient treatment by illuminating the medical settings for allergy-related symptoms. It has many limitations that can make it unable to provide accurate or comprehensive information. Despite these limitations, AI chatbots had the ability to fundamentally alter the field of allergic diseases by increasing patient involvement, improving diagnostic accuracy, personalizing treatment, and applying research findings into clinical practice (<xref ref-type="table" rid="t2">Table 2</xref>). AI chatbots in clinics face challenges and further research is needed to understand their potential uses and potential hazards. While AI can improve patient care and maintain medical expertise, it also requires continuous cooperation between AI creators and allergy experts, requiring further research and understanding of potential risks [<xref ref-type="bibr" rid="B16">16</xref>].</p>
<p id="p-10">AI should be used alongside human supervision and integrated into any system, not as a stand-in. However, some businesses are utilizing AI in inventory management, and its results are promising. AI may significantly influence demand prediction due to the availability of accurate information on the web and the interplay of corporate computers and smart devices, leading to a shift from traditional inventory control methods. Such as Amazon integrates AI and stock management into its forecasting process to compete with other online retailers, requiring administrators to rethink inventory management procedures [<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>]. AI gathers information from various databases about the disease, aiding scientists in making decisions for new and current treatments. It also expedites clinical trials by identifying suitable individuals. AI can optimize physician interactions, provide affordable medical care, enhance practical statistics, guide daily habits, integrate eating and physical activity, and ensure care plan compliance [<xref ref-type="bibr" rid="B19">19</xref>–<xref ref-type="bibr" rid="B21">21</xref>].</p>
</sec>
<sec id="s4">
<title>Benefits and advantages of ChatGPT in pharmacy practice</title>
<p id="p-11">ChatGPT showcases remarkable capabilities in examining pharmaceutical interrelations, notifying pharmacists about conceivable perilous situations, diminishing the likelihood of deleterious drug interactions, curbing diagnostic errors, and enhancing patient well-being [<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>]. Moreover, ChatGPT possesses the capability to facilitate in autonomously verifying prescriptions, as well as guaranteeing adherence to lawful and regulatory obligations. Adequate inventory control is crucial to ensure the seamless functioning of a pharmaceutical establishment. By capitalizing on the potential of ChatGPT, apothecaries can monitor and supervise their stock inventory with enhanced proficiency, thereby optimizing the quantity of supplies and averting any scarcity of medications. Facilitate a resilient association between the pharmacy’s software and ChatGPT API, enabling the AI to retrieve pertinent patient information, including prescribed drug timetables and communication specifics. Craft tailor-made scripts or applications that utilize ChatGPT to generate personalized notification messages based on an individual’s specific medication schedule. Integrate the ChatGPT-generated messages into the pharmacy’s pre-existing communication channels, such as email or SMS services, to methodically send patient notifications [<xref ref-type="bibr" rid="B23">23</xref>] (<xref ref-type="table" rid="t3">Table 3</xref>). ChatGPT has the potential to fulfill the societal objective of eliminating linguistic obstacles, thereby enabling a broader population to create exceptional healthcare literature [<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>]. To delve deeper, the survey witnessed the utmost consensus (77.2%) via the proclamation that pharmacists could derive immense advantages by employing ChatGPT [<xref ref-type="bibr" rid="B22">22</xref>]. The realm of AI wields the adeptness to discern individuals predisposed to re-entry, thereby enabling tailored interventions that amplify the flourishing and outcomes of patients. AI possesses an astonishing capacity to provide instant and precise revelations to adept surgeons, assisting them in evading errors and complexities. The implementation of AI empowers healthcare experts to focus predominantly on delivering exceptional patient care, enhancing safety protocols, and optimizing the overall execution of healthcare provisions [<xref ref-type="bibr" rid="B22">22</xref>].</p>
<table-wrap id="t3">
<label>Table 3</label>
<caption>
<p id="t3-p-1">Summary of the benefits and challenges of ChatGPT in pharmacy practice</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Aspect</th>
<th>Description</th>
<th>References</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="3">Benefits</td>
</tr>
<tr>
<td>    Drug safety</td>
<td>Examines pharmaceutical interrelations, notifies about potential hazards, reduces likelihood of harmful drug interactions</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>]</td>
</tr>
<tr>
<td>    Error reduction</td>
<td>Curbs diagnostic errors, assists in autonomous prescription verification</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>]</td>
</tr>
<tr>
<td>    Patient care</td>
<td>Enhances patient well-being, generates personalized notification messages</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>]</td>
</tr>
<tr>
<td>    Compliance</td>
<td>Ensures adherence to legal and regulatory obligations</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>]</td>
</tr>
<tr>
<td>    Inventory management</td>
<td>Improves stock control and supply optimization</td>
<td>[<xref ref-type="bibr" rid="B23">23</xref>]</td>
</tr>
<tr>
<td>    Communication</td>
<td>Eliminates language barriers in healthcare literature</td>
<td>[<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>]</td>
</tr>
<tr>
<td>    Predictive analytics</td>
<td>Identifies individuals prone to readmission for targeted interventions</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>]</td>
</tr>
<tr>
<td>    Surgical support</td>
<td>Provides instant, accurate insights to skilled surgeons</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>]</td>
</tr>
<tr>
<td>    Healthcare efficiency</td>
<td>Allows professionals to focus more on patient care</td>
<td>[<xref ref-type="bibr" rid="B22">22</xref>]</td>
</tr>
<tr>
<td>    Clinical decision-making</td>
<td>Enhances decision-making through AI algorithms and machine learning</td>
<td>[<xref ref-type="bibr" rid="B28">28</xref>]</td>
</tr>
<tr>
<td colspan="3">Challenges</td>
</tr>
<tr>
<td>    Security</td>
<td>Risk of adversarial attacks</td>
<td>[<xref ref-type="bibr" rid="B4">4</xref>]</td>
</tr>
<tr>
<td>    Misinformation</td>
<td>Potential for spreading false information or propaganda</td>
<td>[<xref ref-type="bibr" rid="B4">4</xref>]</td>
</tr>
<tr>
<td>    Identity theft</td>
<td>Risk of deception due to human-like text generation</td>
<td>[<xref ref-type="bibr" rid="B4">4</xref>]</td>
</tr>
<tr>
<td>    Data quality</td>
<td>Dependence on accuracy and quality of training data, potential for bias</td>
<td>[<xref ref-type="bibr" rid="B26">26</xref>]</td>
</tr>
<tr>
<td>    Privacy</td>
<td>Concerns about extensive use of patient data for AI training</td>
<td>[<xref ref-type="bibr" rid="B28">28</xref>]</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s5">
<title>Challenges and considerations in implementing ChatGPT</title>
<p id="p-12">Just like any sophisticated deep learning system, ChatGPT raises possible apprehensions regarding security. One primary issue revolves around the peril of adversary assaults, wherein a malevolent perpetrator endeavor’s to manipulate the system by introducing detrimental inputs that generate inaccurate or adverse outcomes. One additional concern revolves around the probability of ChatGPT being exploited to spread untruths or propaganda, especially if it becomes integrated into platforms with extensive influence, like social media networks. Furthermore, the ChatGPT’s aptitude in generating text resembling human communication heightens the danger of deception and theft of individual identities [<xref ref-type="bibr" rid="B4">4</xref>] (<xref ref-type="table" rid="t3">Table 3</xref>).</p>
<p id="p-13">The dependability of this system is intricately connected to the precision and excellence of its training data, the particularities of which are veiled in confidentiality and probably put importance on a limited concentration in the domain of medical care and communal wellbeing. This clandestine information could potentially encompass inaccuracies, fostering the assimilation of flawed data, lopsided content, and prejudices throughout the training process [<xref ref-type="bibr" rid="B26">26</xref>]. ChatGPT offers tailored recommendations for educators by analysing data on pedagogical principles and students’ academic achievements. Should you venture on the quest of acquiring knowledge in an unfamiliar linguistic system, you are presented with the extraordinary chance to exploit the immense capabilities of ChatGPT to skilfully obtain personalized recommendations for enhancing your vocabulary, expression, and enunciation. ChatGPT possesses the ability to provide students with superior support in readying themselves for evaluations by conducting in-depth scrutiny of data concerning their previous achievements and favoured methods of learning [<xref ref-type="bibr" rid="B27">27</xref>] (<xref ref-type="table" rid="t3">Table 3</xref>). The integration of AI technologies equips pharmacists with advanced tools and sophisticated systems that empower them to formulate precise and knowledge-driven clinical judgments. By employing AI algorithms and machine learning techniques, pharmacologists can swiftly scrutinize extensive quantities of patient data, encompassing medical documentation, laboratory findings, and drug histories. This enables them to discern potential interactions among pharmaceuticals, assess the safety and efficacy of treatments, and provide astute recommendations tailored to individual patients. Securing the overt endorsement of patients plays a crucial part in alleviating concerns regarding the privacy of information, as healthcare establishments may extensively utilize patient data for the education of advanced computer systems, lacking the procurement of adequate authorization from every individual (<xref ref-type="table" rid="t3">Table 3</xref>) [<xref ref-type="bibr" rid="B28">28</xref>].</p>
</sec>
<sec id="s6">
<title>Some stories of implementing ChatGPT and AI in pharmacy management</title>
<p id="p-14">ChatGPT is a powerful language model that can generate text responses based on prompts. It can be used for various purposes in the pharmacy sector, such as providing customer support, checking drug interactions, validating prescriptions, and advancing drug discovery (see <xref ref-type="table" rid="t4">Table 4</xref>).</p>
<table-wrap id="t4">
<label>Table 4</label>
<caption>
<p id="t4-p-1">Use stories of ChatGPT in pharmacy</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>
<bold>Title</bold>
</th>
<th>
<bold>Country</bold>
</th>
<th>
<bold>Outcome</bold>
</th>
<th>
<bold>Duration</bold>
</th>
<th>
<bold>References</bold>
</th>
</tr>
</thead>
<tbody>
<tr>
<td>ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists’ perception, practice, and concerns</td>
<td>Jordan</td>
<td>Identified benefits and challenges of ChatGPT in pharmacy practice</td>
<td>2 months</td>
<td>[<xref ref-type="bibr" rid="B21">21</xref>]</td>
</tr>
<tr>
<td>Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management</td>
<td>USA</td>
<td>ChatGPT 4.0 accurately solved 39 out of 39 (100%) patient cases in medication therapy management (MTM). It successfully identified drug interactions, provided therapy recommendations, and formulated general management plans, but did not recommend specific dosages. The study suggests that ChatGPT can assist pharmacists in formulating MTM plans to improve overall efficiency and enhance patient safety. The future of the pharmacy profession may depend on integrating AI models like ChatGPT to improve patient care.</td>
<td>N/A</td>
<td>[<xref ref-type="bibr" rid="B30">30</xref>]</td>
</tr>
</tbody>
</table>
</table-wrap>
<p id="p-15">Waterloo’s School of Pharmacy has been using a generative AI platform since early–2018 to enhance experiential learning for pharmacy professionals and students. The platform uses ChatGPT to create realistic patient scenarios for warfarin management, a complex and critical task for pharmacists. The platform allows learners to interact with ChatGPT patients and receive feedback on their decisions [<xref ref-type="bibr" rid="B29">29</xref>].</p>
<p id="p-16">The main outcome of the article “Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management” [<xref ref-type="bibr" rid="B30">30</xref>] is that ChatGPT 4.0 accurately solved 39 out of 39 (100%) patient cases in medication therapy management (MTM). It successfully identified drug interactions, provided therapy recommendations, and formulated general management plans, but did not recommend specific dosages. The study suggests that ChatGPT can assist pharmacists in formulating MTM plans to improve overall efficiency and enhance patient safety. The future of the pharmacy profession may depend on integrating AI models like ChatGPT to improve patient care.</p>
<p id="p-17">Brisk Logic, a software development company, has been emerging ChatGPT practice for drug recognition and progress. ChatGPT can create an advanced, specific mixture based on the correct properties or objects, such as blocking a particular enzyme or being compulsory to a separate effector. ChatGPT can also evaluate the chemical form and pharmaceutical activity of existing medicines and propose developments or options.</p>
</sec>
<sec id="s7">
<title>Future development of ChatGPT in pharmacy management</title>
<p id="p-18">Certain changes are required to fully realise ChatGPT’s promise in pharmacy management. One important focus is to increase the accuracy of pharmaceutical guidance. The discussion of AI in health systems concludes by addressing many implementation challenges with AI both within and outside the health industry. Data protection, societal difficulties, ethical issues, hacking issues, and developer issues were some of the challenges to successfully applying AI in the medical industry [<xref ref-type="bibr" rid="B31">31</xref>].</p>
<p id="p-19">Moreover, research into AI governance and regulatory frameworks is critical for understanding their impact on innovation, healthcare costs, access to treatment, and health inequities. Decentralized data management techniques, such as Federated Learning, can improve data security and scalability in healthcare AI. Exploring synthetic health data generation techniques and analyzing their applications can provide a broad training dataset for constructing strong AI models in healthcare [<xref ref-type="bibr" rid="B32">32</xref>].</p>
</sec>
<sec id="s8">
<title>Future directions and opportunities</title>
<p id="p-20">The effectiveness of ChatGPT and other NLP technologies in pharmacy management may be further explored and built upon in future studies. Researchers may be motivated to create new procedures based on the study’s findings in order to improve ChatGPT’s predictiveness and accuracy while handling medication administration [<xref ref-type="bibr" rid="B33">33</xref>]. More detailed and follow-up inquiries are required as ChatGPT’s accuracy in producing accurate answers often decreases with case complexity. Nevertheless, it was effective in pointing out possible combinations that would exacerbate a patient’s symptoms and offered suitable treatment strategies. The majority of drug-drug, drug-substance, and drug-disease interactions were covered with full precision in every case. ChatGPT has the ability and accuracy to solve clinical patient cases with different complexity levels [<xref ref-type="bibr" rid="B34">34</xref>]. It demonstrated its potential to optimize clinical workflow, leading to cost savings and improved healthcare delivery efficiency. ChatGPT’s capacity to generate effective discharge summaries can reduce documentation load in the healthcare industry. It also has the potential to revolutionize healthcare delivery by improving diagnostics, predicting illness risk and outcome, and discovering new drugs, among other translational research fields [<xref ref-type="bibr" rid="B33">33</xref>].</p>
<p id="p-21">Additionally, there is the potential for enhanced health literacy and tailored care through the public’s easy access to clear health information. ChatGPT comments illustrating the need of consulting healthcare practitioners and other trustworthy sources in particular scenarios served as an example of its value [<xref ref-type="bibr" rid="B35">35</xref>]. ChatGPT has the potential to revolutionize healthcare by improving diagnostics, predicting illness risk and outcome, and discovering new drugs [<xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B37">37</xref>]. It has shown moderate accuracy in breast cancer screening and discomfort assessment, suggesting potential for radiology decision-making [<xref ref-type="bibr" rid="B38">38</xref>]. ChatGPT can increase health literacy and advance personalized treatment by making health information accessible to the general population [<xref ref-type="bibr" rid="B39">39</xref>]. However, consulting healthcare professionals and trustworthy sources is essential in specific circumstances, emphasizing the importance of trust in healthcare [<xref ref-type="bibr" rid="B40">40</xref>].</p>
<p id="p-22">A number of recent studies have emphasized the possible advantages of using ChatGPT in pharmacy education. Students who struggle to grasp pharmaceutical and medical ideas might benefit from ChatGPT’s simpler explanations of complicated subjects and medical jargon. Additionally, it can provide interactive platforms for the development of facial expression and emotional communication—two abilities that are crucial for patient counseling [<xref ref-type="bibr" rid="B8">8</xref>]. By integrating AI-based technologies, learning outcomes, health literacy, and evidence-based practice may all be improved. In addition to collecting data on health-related subjects including patient satisfaction, healthcare use, and health habits, ChatGPT may also be used to educate patients, healthcare practitioners, and researchers about a variety of medical disorders [<xref ref-type="bibr" rid="B41">41</xref>]. All things considered, ChatGPT can lead to better medical results. Patients can engage in research projects using ChatGPT by offering their opinions, involvement, and suggestions for study topics. This database takes into account the requirements and concerns of patients, makes use of recent illness prevalence, and guarantees patient-centered investigations [<xref ref-type="bibr" rid="B42">42</xref>].</p>
<p id="p-23">Participants in the current survey showed a keen interest in using AI to the pharmaceutical industry. According to a Saudi Arabian research [<xref ref-type="bibr" rid="B43">43</xref>], 87% of pharmacists expressed a strong readiness to employ telepharmacy technology. Comparatively, in earlier research carried out in China and Pakistan [<xref ref-type="bibr" rid="B44">44</xref>], doctors and medical students also indicated a strong desire to use AI in the field of medicine [<xref ref-type="bibr" rid="B45">45</xref>]. Participants in the current study showed the greatest desire to use AI (82.3%) to connect with healthcare providers from home and receive primary care. This makes sense because finding the most inexpensive healthcare solutions and easing contact with healthcare professionals are two of AI’s most beneficial applications [<xref ref-type="bibr" rid="B46">46</xref>].</p>
<p id="p-24">The majority of pharmacists, according to the survey, are enthusiastic about using AI in the pharmaceutical industry, demonstrating a favorable attitude toward AI. However, the absence of technology and software specifically dedicated to AI, the requirement for human supervision, and the high operating expenses are obstacles to its application. Healthcare authorities must provide financial assistance in order to guarantee that AI technology is accessible in community pharmacy settings. Experienced pharmacists are more willing to use AI, maybe because they are aware of how labor- and time-intensive some activities might be. Their opinion of AI technology may change as a result, and they may be more inclined to employ it in the future [<xref ref-type="bibr" rid="B47">47</xref>].</p>
</sec>
<sec id="s9">
<title>Conclusions</title>
<p id="p-25">ChatGPT can be applied in pharmacy for customer support, drug interaction checks, prescription validation, drug discovery, and more. It can enhance efficiency and accuracy in many pharmacy workflows. Benefits of ChatGPT include improving patient counseling, reducing medication errors, optimizing inventory management, and generating personalized notifications. It can also aid pharmacy education. However, there are valid concerns about security, reliability, privacy, dependence on AI, and lack of human discretion. Careful implementation with human oversight is crucial. Future opportunities lie in improving ChatGPT’s accuracy, incorporating it into pharmacy curricula, enhancing patient care and engagement, and advancing research through its generative capabilities.</p>
<p id="p-26">Overall, ChatGPT is a promising AI technology that can transform certain aspects of pharmacy management if thoughtfully implemented. More research and guidelines are needed to realize its full potential while addressing ethical concerns. A balanced, evidence-based approach is key.</p>
<p id="p-27">In summary, ChatGPT offers exciting new capabilities but also raises important considerations for the pharmacy field. Further exploration of its responsible integration can pave the way for improved pharmaceutical care and outcomes.</p>
</sec>
</body>
<back>
<glossary>
<title>Abbreviations</title>
<def-list>
<def-item>
<term>AI</term>
<def>
<p>artificial intelligence</p>
</def>
</def-item>
<def-item>
<term>NLP</term>
<def>
<p>natural language processing</p>
</def>
</def-item>
</def-list>
</glossary>
<sec id="s10">
<title>Declarations</title>
<sec id="t-10-1">
<title>Acknowledgments</title>
<p>The authors would like to thank Himanshu Sharma (Assistant Professor, Teerthanker Mahaveer University, Moradabad) for all of his guidance and support.</p>
</sec>
<sec id="t-10-2">
<title>Author contributions</title>
<p>AAN: Conceptualization, Writing—original draft. MDIAF and SMM: Writing—review &amp; editing. TST: Writing—original draft, Data curation. AAS: Validation, Writing—review &amp; editing, Software. All authors read and approved the submitted version.</p>
</sec>
<sec id="t-10-3" sec-type="COI-statement">
<title>Conflicts of interest</title>
<p>The author declares that there are no conflicts of interest.</p>
</sec>
<sec id="t-10-4">
<title>Ethical approval</title>
<p>Not applicable.</p>
</sec>
<sec id="t-10-5">
<title>Consent to participate</title>
<p>Not applicable.</p>
</sec>
<sec id="t-10-6">
<title>Consent to publication</title>
<p>Not applicable.</p>
</sec>
<sec id="t-10-7" sec-type="data-availability">
<title>Availability of data and materials</title>
<p>Not applicable.</p>
</sec>
<sec id="t-10-8">
<title>Funding</title>
<p>Not applicable.</p>
</sec>
<sec id="t-10-9">
<title>Copyright</title>
<p>© The Author(s) 2024.</p>
</sec>
</sec>
<ref-list>
<ref id="B1">
<label>1</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Patterson</surname>
<given-names>CJ</given-names>
</name>
</person-group>
<article-title>Best practices in specialty pharmacy management</article-title>
<source>J Manag Care Pharm</source>
<year iso-8601-date="2013">2013</year>
<volume>19</volume>
<fpage>42</fpage>
<lpage>8</lpage>
<pub-id pub-id-type="doi">10.18553/jmcp.2013.19.1.42</pub-id>
<pub-id pub-id-type="pmid">23383699</pub-id>
<pub-id pub-id-type="pmcid">PMC10438197</pub-id>
</element-citation>
</ref>
<ref id="B2">
<label>2</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Fahim</surname>
<given-names>MIA</given-names>
</name>
<name>
<surname>Tonny</surname>
<given-names>TS</given-names>
</name>
<name>
<surname>Al</surname>
<given-names>Noman A</given-names>
</name>
</person-group>
<article-title>Realizing the potential of AI in pharmacy practice: Barriers and pathways to adoption</article-title>
<source>Intell Pharm</source>
<year iso-8601-date="2024">2024</year>
<volume>2</volume>
<fpage>308</fpage>
<lpage>11</lpage>
<pub-id pub-id-type="doi">10.1016/j.ipha.2024.02.003</pub-id>
</element-citation>
</ref>
<ref id="B3">
<label>3</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Rollins</surname>
<given-names>BL</given-names>
</name>
<name>
<surname>Gunturi</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Sullivan</surname>
<given-names>D</given-names>
</name>
</person-group>
<article-title>A pharmacy business management simulation exercise as a practical application of business management material and principles</article-title>
<source>Am J Pharm Educ</source>
<year iso-8601-date="2014">2014</year>
<volume>78</volume>
<elocation-id>62</elocation-id>
<pub-id pub-id-type="doi">10.5688/ajpe78362</pub-id>
<pub-id pub-id-type="pmid">24761023</pub-id>
<pub-id pub-id-type="pmcid">PMC3996394</pub-id>
</element-citation>
</ref>
<ref id="B4">
<label>4</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Deng</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Lin</surname>
<given-names>Y</given-names>
</name>
</person-group>
<article-title>The benefits and challenges of ChatGPT: An overview</article-title>
<source>Frontiers Computing Intell Syst</source>
<year iso-8601-date="2023">2023</year>
<volume>2</volume>
<fpage>81</fpage>
<lpage>3</lpage>
<pub-id pub-id-type="doi">10.54097/fcis.v2i2.4465</pub-id>
</element-citation>
</ref>
<ref id="B5">
<label>5</label>
<element-citation publication-type="web">
<person-group person-group-type="author">
<name>
<surname>Hariri</surname>
<given-names>W</given-names>
</name>
</person-group>
<article-title>Unlocking the potential of ChatGPT: A comprehensive exploration of its applications, advantages, limitations, and future directions in natural language processing</article-title>
<comment>arXiv:2304.02017v5 [Preprint]. 2024 [cited 2023 Oct 1]. Available from: <uri xlink:href="https://arxiv.org/abs/2304.02017v5">https://arxiv.org/abs/2304.02017v5</uri></comment>
</element-citation>
</ref>
<ref id="B6">
<label>6</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhai</surname>
<given-names>X</given-names>
</name>
</person-group>
<article-title>ChatGPT for next generation science learning</article-title>
<source>XRDS: Crossroads, The ACM Mag Stud</source>
<year iso-8601-date="2023">2023</year>
<volume>29</volume>
<fpage>42</fpage>
<lpage>6</lpage>
<pub-id pub-id-type="doi">10.1145/3589649</pub-id>
</element-citation>
</ref>
<ref id="B7">
<label>7</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhai</surname>
<given-names>X</given-names>
</name>
</person-group>
<article-title>ChatGPT user experience: Implications for education</article-title>
<source>Available at SSRN 4312418</source>
<year iso-8601-date="2022">2022</year>
<pub-id pub-id-type="doi">10.2139/ssrn.4312418</pub-id>
</element-citation>
</ref>
<ref id="B8">
<label>8</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sallam</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Salim</surname>
<given-names>NA</given-names>
</name>
<name>
<surname>Barakat</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Al-Tammemi</surname>
<given-names>AB</given-names>
</name>
</person-group>
<article-title>ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations</article-title>
<source>Narra J</source>
<year iso-8601-date="2023">2023</year>
<volume>3</volume>
<elocation-id>e103</elocation-id>
<pub-id pub-id-type="doi">10.52225/narra.v3i1.103</pub-id>
<pub-id pub-id-type="pmid">38450035</pub-id>
<pub-id pub-id-type="pmcid">PMC10914078</pub-id>
</element-citation>
</ref>
<ref id="B9">
<label>9</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>George</surname>
<given-names>AS</given-names>
</name>
<name>
<surname>George</surname>
<given-names>ASH</given-names>
</name>
</person-group>
<article-title>A review of ChatGPT AI’s impact on several business sectors</article-title>
<source>Partn Univers Inter Innov J</source>
<year iso-8601-date="2023">2023</year>
<volume>1</volume>
<fpage>9</fpage>
<lpage>23</lpage>
<pub-id pub-id-type="doi">10.5281/zenodo.7644359</pub-id>
</element-citation>
</ref>
<ref id="B10">
<label>10</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kleebayoon</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Wiwanitkit</surname>
<given-names>V</given-names>
</name>
</person-group>
<article-title>ChatGPT-4, Medical Education, and Clinical Exposure Challenges</article-title>
<source>Indian J Orthop</source>
<year iso-8601-date="2023">2023</year>
<volume>57</volume>
<elocation-id>1912</elocation-id>
<pub-id pub-id-type="doi">10.1007/s43465-023-00997-1</pub-id>
<pub-id pub-id-type="pmid">37881293</pub-id>
</element-citation>
</ref>
<ref id="B11">
<label>11</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Eysenbach</surname>
<given-names>G</given-names>
</name>
</person-group>
<article-title>The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers</article-title>
<source>JMIR Med Educ</source>
<year iso-8601-date="2023">2023</year>
<volume>9</volume>
<elocation-id>e46885</elocation-id>
<pub-id pub-id-type="doi">10.2196/46885</pub-id>
<pub-id pub-id-type="pmid">36863937</pub-id>
<pub-id pub-id-type="pmcid">PMC10028514</pub-id>
</element-citation>
</ref>
<ref id="B12">
<label>12</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Lehmann</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Aslani</surname>
<given-names>P</given-names>
</name>
<name>
<surname>Ahmed</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Celio</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Gauchet</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Bedouch</surname>
<given-names>P</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Assessing medication adherence: options to consider</article-title>
<source>Int J Clin Pharm</source>
<year iso-8601-date="2014">2014</year>
<volume>36</volume>
<fpage>55</fpage>
<lpage>69</lpage>
<pub-id pub-id-type="doi">10.1007/s11096-013-9865-x</pub-id>
<pub-id pub-id-type="pmid">24166659</pub-id>
</element-citation>
</ref>
<ref id="B13">
<label>13</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Babel</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Taneja</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Mondello</surname>
<given-names>Malvestiti F</given-names>
</name>
<name>
<surname>Monaco</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Donde</surname>
<given-names>S</given-names>
</name>
</person-group>
<article-title>Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases</article-title>
<source>Front Digit Health</source>
<year iso-8601-date="2021">2021</year>
<volume>3</volume>
<elocation-id>669869</elocation-id>
<pub-id pub-id-type="doi">10.3389/fdgth.2021.669869</pub-id>
<pub-id pub-id-type="pmid">34713142</pub-id>
<pub-id pub-id-type="pmcid">PMC8521858</pub-id>
</element-citation>
</ref>
<ref id="B14">
<label>14</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Palmer</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Marengoni</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Forjaz</surname>
<given-names>MJ</given-names>
</name>
<name>
<surname>Jureviciene</surname>
<given-names>E</given-names>
</name>
<name>
<surname>Laatikainen</surname>
<given-names>T</given-names>
</name>
<name>
<surname>Mammarella</surname>
<given-names>F</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Multimorbidity care model: Recommendations from the consensus meeting of the Joint Action on Chronic Diseases and Promoting Healthy Ageing across the Life Cycle (JA-CHRODIS)</article-title>
<source>Health Policy</source>
<year iso-8601-date="2018">2018</year>
<volume>122</volume>
<fpage>4</fpage>
<lpage>11</lpage>
<pub-id pub-id-type="doi">10.1016/j.healthpol.2017.09.006</pub-id>
<pub-id pub-id-type="pmid">28967492</pub-id>
</element-citation>
</ref>
<ref id="B15">
<label>15</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Juhi</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Pipil</surname>
<given-names>N</given-names>
</name>
<name>
<surname>Santra</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Mondal</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Behera</surname>
<given-names>JK</given-names>
</name>
<name>
<surname>Mondal</surname>
<given-names>H</given-names>
</name>
</person-group>
<article-title>The Capability of ChatGPT in Predicting and Explaining Common Drug-Drug Interactions</article-title>
<source>Cureus</source>
<year iso-8601-date="2023">2023</year>
<volume>15</volume>
<elocation-id>e36272</elocation-id>
<pub-id pub-id-type="doi">10.7759/cureus.36272</pub-id>
<pub-id pub-id-type="pmid">37073184</pub-id>
<pub-id pub-id-type="pmcid">PMC10105894</pub-id>
</element-citation>
</ref>
<ref id="B16">
<label>16</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Goktas</surname>
<given-names>P</given-names>
</name>
<name>
<surname>Karakaya</surname>
<given-names>G</given-names>
</name>
<name>
<surname>Kalyoncu</surname>
<given-names>AF</given-names>
</name>
<name>
<surname>Damadoglu</surname>
<given-names>E</given-names>
</name>
</person-group>
<article-title>Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going?</article-title>
<source>J Allergy Clin Immunol Pract</source>
<year iso-8601-date="2023">2023</year>
<volume>11</volume>
<fpage>2697</fpage>
<lpage>700</lpage>
<pub-id pub-id-type="doi">10.1016/j.jaip.2023.05.042</pub-id>
<pub-id pub-id-type="pmid">37301435</pub-id>
</element-citation>
</ref>
<ref id="B17">
<label>17</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Lingam</surname>
<given-names>YK</given-names>
</name>
</person-group>
<article-title>The role of Artificial Intelligence (AI) in making accurate stock decisions in E-commerce industry</article-title>
<source>Int J Adv Res Ideas Innov Technol</source>
<year iso-8601-date="2018">2018</year>
<volume>4</volume>
<fpage>2281</fpage>
<lpage>6</lpage>
</element-citation>
</ref>
<ref id="B18">
<label>18</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Madhuri</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Praveen</surname>
<given-names>KB</given-names>
</name>
<name>
<surname>Pradyumna</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Prateek</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Pragathi</surname>
<given-names>G</given-names>
</name>
</person-group>
<article-title>Inventory management using machine learning</article-title>
<source>Int J Eng Res &amp; Technol (IJERT)</source>
<year iso-8601-date="2020">2020</year>
<volume>9</volume>
<fpage>866</fpage>
<lpage>9</lpage>
<pub-id pub-id-type="doi">10.17577/IJERTV9IS060661</pub-id>
</element-citation>
</ref>
<ref id="B19">
<label>19</label>
<element-citation publication-type="web">
<article-title>Artificial Intelligence in Pharmacy: Are You Ready? [Internet]</article-title>
<comment>Wolters Kluwer N.V.; c2024 [cited 2023 Oct 31]. Available from: <uri xlink:href="https://www.wolterskluwer.com/en/expert-insights/artificial-intelligence-in-pharmacy-are-you-ready">https://www.wolterskluwer.com/en/expert-insights/artificial-intelligence-in-pharmacy-are-you-ready</uri></comment>
</element-citation>
</ref>
<ref id="B20">
<label>20</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Biswas</surname>
<given-names>S</given-names>
</name>
</person-group>
<article-title>Prospective Role of ChatGPT in Pharmacy: According to ChatGPT</article-title>
<source>Available at SSRN 4405384</source>
<year iso-8601-date="2023">2023</year>
<pub-id pub-id-type="doi">10.32388/QGMSQG</pub-id>
</element-citation>
</ref>
<ref id="B21">
<label>21</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Abu</surname>
<given-names>Hammour K</given-names>
</name>
<name>
<surname>Alhamad</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Al-Ashwal</surname>
<given-names>FY</given-names>
</name>
<name>
<surname>Halboup</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Abu</surname>
<given-names>Farha R</given-names>
</name>
<name>
<surname>Abu</surname>
<given-names>Hammour A</given-names>
</name>
</person-group>
<article-title>ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists’ perception, practice, and concerns</article-title>
<source>J Pharm Policy Pract</source>
<year iso-8601-date="2023">2023</year>
<volume>16</volume>
<elocation-id>115</elocation-id>
<pub-id pub-id-type="doi">10.1186/s40545-023-00624-2</pub-id>
<pub-id pub-id-type="pmid">37789443</pub-id>
<pub-id pub-id-type="pmcid">PMC10548710</pub-id>
</element-citation>
</ref>
<ref id="B22">
<label>22</label>
<element-citation publication-type="web">
<article-title>ChatGPT—What does AI know about Patient Safety? [Internet]</article-title>
<comment>Brussels: European Society of Anaesthesiology and Intensive Care; c2024 [cited 2023 Oct 29]. Available from: <uri xlink:href="https://esaic.org/chatgpt-what-does-ai-know-about-patient-safety/">https://esaic.org/chatgpt-what-does-ai-know-about-patient-safety/</uri></comment>
</element-citation>
</ref>
<ref id="B23">
<label>23</label>
<element-citation publication-type="web">
<article-title>These 6 ChatGPT Pharmacy Applications will Change your Life [Internet]</article-title>
<comment>PHARMACY MENTOR™ 2016–24; [cited 2023 Oct 29]. Available from: <uri xlink:href="https://www.pharmacymentor.com/chatgpt-pharmacy/">https://www.pharmacymentor.com/chatgpt-pharmacy/</uri></comment>
</element-citation>
</ref>
<ref id="B24">
<label>24</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dave</surname>
<given-names>T</given-names>
</name>
<name>
<surname>Athaluri</surname>
<given-names>SA</given-names>
</name>
<name>
<surname>Singh</surname>
<given-names>S</given-names>
</name>
</person-group>
<article-title>ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations</article-title>
<source>Front Artif Intell</source>
<year iso-8601-date="2023">2023</year>
<volume>6</volume>
<elocation-id>1169595</elocation-id>
<pub-id pub-id-type="doi">10.3389/frai.2023.1169595</pub-id>
<pub-id pub-id-type="pmid">37215063</pub-id>
<pub-id pub-id-type="pmcid">PMC10192861</pub-id>
</element-citation>
</ref>
<ref id="B25">
<label>25</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>van Dis</surname>
<given-names>EAM</given-names>
</name>
<name>
<surname>Bollen</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Zuidema</surname>
<given-names>W</given-names>
</name>
<name>
<surname>van Rooij</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Bockting</surname>
<given-names>CL</given-names>
</name>
</person-group>
<article-title>ChatGPT: five priorities for research</article-title>
<source>Nature</source>
<year iso-8601-date="2023">2023</year>
<volume>614</volume>
<fpage>224</fpage>
<lpage>6</lpage>
<pub-id pub-id-type="doi">10.1038/d41586-023-00288-7</pub-id>
</element-citation>
</ref>
<ref id="B26">
<label>26</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Wang</surname>
<given-names>X</given-names>
</name>
<name>
<surname>Sanders</surname>
<given-names>HM</given-names>
</name>
<name>
<surname>Liu</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Seang</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Tran</surname>
<given-names>BX</given-names>
</name>
<name>
<surname>Atanasov</surname>
<given-names>AG</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>ChatGPT: promise and challenges for deployment in low- and middle-income countries</article-title>
<source>Lancet Reg Health West Pac</source>
<year iso-8601-date="2023">2023</year>
<volume>41</volume>
<elocation-id>100905</elocation-id>
<pub-id pub-id-type="doi">10.1016/j.lanwpc.2023.100905</pub-id>
<pub-id pub-id-type="pmid">37731897</pub-id>
<pub-id pub-id-type="pmcid">PMC10507635</pub-id>
</element-citation>
</ref>
<ref id="B27">
<label>27</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Gill</surname>
<given-names>SS</given-names>
</name>
<name>
<surname>Kaur</surname>
<given-names>R</given-names>
</name>
</person-group>
<article-title>ChatGPT: Vision and challenges</article-title>
<source>Internet Things Cyber-Phys Syst</source>
<year iso-8601-date="2023">2023</year>
<volume>3</volume>
<fpage>262</fpage>
<lpage>71</lpage>
<pub-id pub-id-type="doi">10.1016/j.iotcps.2023.05.004</pub-id>
</element-citation>
</ref>
<ref id="B28">
<label>28</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Chalasani</surname>
<given-names>SH</given-names>
</name>
<name>
<surname>Syed</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Ramesh</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Patil</surname>
<given-names>V</given-names>
</name>
<name>
<surname>Pramod</surname>
<given-names>Kumar TM</given-names>
</name>
</person-group>
<article-title>Artificial intelligence in the field of pharmacy practice: A literature review</article-title>
<source>Explor Res Clin Soc Pharm</source>
<year iso-8601-date="2023">2023</year>
<volume>12</volume>
<elocation-id>100346</elocation-id>
<pub-id pub-id-type="doi">10.1016/j.rcsop.2023.100346</pub-id>
<pub-id pub-id-type="pmid">37885437</pub-id>
<pub-id pub-id-type="pmcid">PMC10598710</pub-id>
</element-citation>
</ref>
<ref id="B29">
<label>29</label>
<element-citation publication-type="web">
<article-title>Generative AI: ChatGPT enhances experiential learning in pharmacy [Internet]</article-title>
<comment>Waterloo: University of Waterloo 1992-2023; c2024 [cited 2023 Dec 26]. Available from: <uri xlink:href="https://uwaterloo.ca/science/news/generative-ai-chatgpt-enhances-experiential-learning">https://uwaterloo.ca/science/news/generative-ai-chatgpt-enhances-experiential-learning</uri></comment>
</element-citation>
</ref>
<ref id="B30">
<label>30</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Roosan</surname>
<given-names>D</given-names>
</name>
<name>
<surname>Padua</surname>
<given-names>P</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>R</given-names>
</name>
<name>
<surname>Khan</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Verzosa</surname>
<given-names>C</given-names>
</name>
<name>
<surname>Wu</surname>
<given-names>Y</given-names>
</name>
</person-group>
<article-title>Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management</article-title>
<source>J Am Pharm Assoc (2003)</source>
<year iso-8601-date="2024">2024</year>
<volume>64</volume>
<fpage>422</fpage>
<lpage>8.e8</lpage>
<pub-id pub-id-type="doi">10.1016/j.japh.2023.11.023</pub-id>
<pub-id pub-id-type="pmid">38049066</pub-id>
</element-citation>
</ref>
<ref id="B31">
<label>31</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Khan</surname>
<given-names>B</given-names>
</name>
<name>
<surname>Fatima</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Qureshi</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Kumar</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Hanan</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Hussain</surname>
<given-names>J</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector</article-title>
<source>Biomed Mater Devices</source>
<year iso-8601-date="2023">2023</year>
<volume>1</volume>
<fpage>731</fpage>
<lpage>8</lpage>
<pub-id pub-id-type="doi">10.1007/s44174-023-00063-2</pub-id>
<pub-id pub-id-type="pmid">36785697</pub-id>
<pub-id pub-id-type="pmcid">PMC9908503</pub-id>
</element-citation>
</ref>
<ref id="B32">
<label>32</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Williamson</surname>
<given-names>SM</given-names>
</name>
<name>
<surname>Prybutok</surname>
<given-names>V</given-names>
</name>
</person-group>
<article-title>Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare</article-title>
<source>Appl Sci</source>
<year iso-8601-date="2024">2024</year>
<volume>14</volume>
<elocation-id>675</elocation-id>
<pub-id pub-id-type="doi">10.3390/app14020675</pub-id>
</element-citation>
</ref>
<ref id="B33">
<label>33</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Bagde</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Dhopte</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Alam</surname>
<given-names>MK</given-names>
</name>
<name>
<surname>Basri</surname>
<given-names>R</given-names>
</name>
</person-group>
<article-title>A systematic review and meta-analysis on ChatGPT and its utilization in medical and dental research</article-title>
<source>Heliyon</source>
<year iso-8601-date="2023">2023</year>
<volume>9</volume>
<elocation-id>e23050</elocation-id>
<pub-id pub-id-type="doi">10.1016/j.heliyon.2023.e23050</pub-id>
<pub-id pub-id-type="pmid">38144348</pub-id>
<pub-id pub-id-type="pmcid">PMC10746423</pub-id>
</element-citation>
</ref>
<ref id="B34">
<label>34</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ge</surname>
<given-names>W</given-names>
</name>
<name>
<surname>Cao</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Shen</surname>
<given-names>F</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>Y</given-names>
</name>
<name>
<surname>Ren</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Wang</surname>
<given-names>X</given-names>
</name>
</person-group>
<article-title>Rapid self-healing, stretchable, moldable, antioxidant and antibacterial tannic acid-cellulose nanofibril composite hydrogels</article-title>
<source>Carbohydr Polym</source>
<year iso-8601-date="2019">2019</year>
<volume>224</volume>
<elocation-id>115147</elocation-id>
<pub-id pub-id-type="doi">10.1016/j.carbpol.2019.115147</pub-id>
<pub-id pub-id-type="pmid">31472826</pub-id>
</element-citation>
</ref>
<ref id="B35">
<label>35</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sallam</surname>
<given-names>M</given-names>
</name>
</person-group>
<article-title>The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations</article-title>
<source>medRxiv</source>
<year iso-8601-date="2023">2023</year>
<volume>11</volume>
<elocation-id>887</elocation-id>
<pub-id pub-id-type="doi">10.3390/healthcare11060887</pub-id>
</element-citation>
</ref>
<ref id="B36">
<label>36</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Holzinger</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Keiblinger</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Holub</surname>
<given-names>P</given-names>
</name>
<name>
<surname>Zatloukal</surname>
<given-names>K</given-names>
</name>
<name>
<surname>Müller</surname>
<given-names>H</given-names>
</name>
</person-group>
<article-title>AI for life: Trends in artificial intelligence for biotechnology</article-title>
<source>N Biotechnol</source>
<year iso-8601-date="2023">2023</year>
<volume>74</volume>
<fpage>16</fpage>
<lpage>24</lpage>
<pub-id pub-id-type="doi">10.1016/j.nbt.2023.02.001</pub-id>
<pub-id pub-id-type="pmid">36754147</pub-id>
</element-citation>
</ref>
<ref id="B37">
<label>37</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Mann</surname>
<given-names>DL</given-names>
</name>
</person-group>
<article-title>Artificial Intelligence Discusses the Role of Artificial Intelligence in Translational Medicine: A <italic>JACC: Basic to Translational Science</italic> Interview With ChatGPT</article-title>
<source>JACC Basic Transl Sci</source>
<year iso-8601-date="2023">2023</year>
<volume>8</volume>
<fpage>221</fpage>
<lpage>3</lpage>
<pub-id pub-id-type="doi">10.1016/j.jacbts.2023.01.001</pub-id>
<pub-id pub-id-type="pmid">36908674</pub-id>
<pub-id pub-id-type="pmcid">PMC9998448</pub-id>
</element-citation>
</ref>
<ref id="B38">
<label>38</label>
<element-citation publication-type="web">
<person-group person-group-type="author">
<name>
<surname>Rao</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Kim</surname>
<given-names>J</given-names>
</name>
<name>
<surname>Kamineni</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Pang</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Lie</surname>
<given-names>W</given-names>
</name>
<name>
<surname>Succi</surname>
<given-names>MD</given-names>
</name>
</person-group>
<article-title>Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making</article-title>
<comment>medRxiv 2023.02.02.23285399 [Preprint]. 2023 [cited 2023 Oct 29]. Available from: <uri xlink:href="https://www.medrxiv.org/content/10.1101/2023.02.02.23285399v1">https://www.medrxiv.org/content/10.1101/2023.02.02.23285399v1</uri></comment>
</element-citation>
</ref>
<ref id="B39">
<label>39</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kung</surname>
<given-names>TH</given-names>
</name>
<name>
<surname>Cheatham</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Medenilla</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Sillos</surname>
<given-names>C</given-names>
</name>
<name>
<surname>De</surname>
<given-names>Leon L</given-names>
</name>
<name>
<surname>Elepaño</surname>
<given-names>C</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models</article-title>
<source>PLOS Digit Health</source>
<year iso-8601-date="2023">2023</year>
<volume>2</volume>
<elocation-id>e0000198</elocation-id>
<pub-id pub-id-type="doi">10.1371/journal.pdig.0000198</pub-id>
<pub-id pub-id-type="pmid">36812645</pub-id>
<pub-id pub-id-type="pmcid">PMC9931230</pub-id>
</element-citation>
</ref>
<ref id="B40">
<label>40</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<collab>ChatGPT Generative Pre-trained Transformer</collab>
<name>
<surname>Zhavoronkov</surname>
<given-names>A</given-names>
</name>
</person-group>
<article-title>Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective</article-title>
<source>Oncoscience</source>
<year iso-8601-date="2022">2022</year>
<volume>9</volume>
<fpage>82</fpage>
<lpage>4</lpage>
<pub-id pub-id-type="doi">10.18632/oncoscience.571</pub-id>
<pub-id pub-id-type="pmid">36589923</pub-id>
<pub-id pub-id-type="pmcid">PMC9796173</pub-id>
</element-citation>
</ref>
<ref id="B41">
<label>41</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Islam</surname>
<given-names>MR</given-names>
</name>
<name>
<surname>Urmi</surname>
<given-names>TJ</given-names>
</name>
<name>
<surname>Mosharrafa</surname>
<given-names>RA</given-names>
</name>
<name>
<surname>Rahman</surname>
<given-names>MS</given-names>
</name>
<name>
<surname>Kadir</surname>
<given-names>MF</given-names>
</name>
</person-group>
<article-title>Role of ChatGPT in health science and research: A correspondence addressing potential application</article-title>
<source>Health Sci Rep</source>
<year iso-8601-date="2023">2023</year>
<volume>6</volume>
<elocation-id>e1625</elocation-id>
<pub-id pub-id-type="doi">10.1002/hsr2.1625</pub-id>
<pub-id pub-id-type="pmid">37841943</pub-id>
<pub-id pub-id-type="pmcid">PMC10568002</pub-id>
</element-citation>
</ref>
<ref id="B42">
<label>42</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sallam</surname>
<given-names>M</given-names>
</name>
</person-group>
<article-title>ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns</article-title>
<source>Healthcare (Basel)</source>
<year iso-8601-date="2023">2023</year>
<volume>11</volume>
<elocation-id>887</elocation-id>
<pub-id pub-id-type="doi">10.3390/healthcare11060887</pub-id>
<pub-id pub-id-type="pmid">36981544</pub-id>
<pub-id pub-id-type="pmcid">PMC10048148</pub-id>
</element-citation>
</ref>
<ref id="B43">
<label>43</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Dat</surname>
<given-names>TV</given-names>
</name>
<name>
<surname>Tran</surname>
<given-names>TD</given-names>
</name>
<name>
<surname>My</surname>
<given-names>NT</given-names>
</name>
<name>
<surname>Nguyen</surname>
<given-names>TTH</given-names>
</name>
<name>
<surname>Quang</surname>
<given-names>NNA</given-names>
</name>
<name>
<surname>Tra</surname>
<given-names>Vo Nguyen M</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Pharmacists’ Perspectives on the Use of Telepharmacy in Response to COVID-19 Pandemic in Ho Chi Minh City, Vietnam</article-title>
<source>J Pharm Technol</source>
<year iso-8601-date="2022">2022</year>
<volume>38</volume>
<fpage>106</fpage>
<lpage>14</lpage>
<pub-id pub-id-type="doi">10.1177/87551225221076327</pub-id>
<pub-id pub-id-type="pmid">35571348</pub-id>
<pub-id pub-id-type="pmcid">PMC9096850</pub-id>
</element-citation>
</ref>
<ref id="B44">
<label>44</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Chen</surname>
<given-names>M</given-names>
</name>
<name>
<surname>Zhang</surname>
<given-names>B</given-names>
</name>
<name>
<surname>Cai</surname>
<given-names>Z</given-names>
</name>
<name>
<surname>Seery</surname>
<given-names>S</given-names>
</name>
<name>
<surname>Gonzalez</surname>
<given-names>MJ</given-names>
</name>
<name>
<surname>Ali</surname>
<given-names>NM</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey</article-title>
<source>Front Med (Lausanne)</source>
<year iso-8601-date="2022">2022</year>
<volume>9</volume>
<elocation-id>990604</elocation-id>
<pub-id pub-id-type="doi">10.3389/fmed.2022.990604</pub-id>
<pub-id pub-id-type="pmid">36117979</pub-id>
<pub-id pub-id-type="pmcid">PMC9472134</pub-id>
</element-citation>
</ref>
<ref id="B45">
<label>45</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Ahmed</surname>
<given-names>Z</given-names>
</name>
<name>
<surname>Bhinder</surname>
<given-names>KK</given-names>
</name>
<name>
<surname>Tariq</surname>
<given-names>A</given-names>
</name>
<name>
<surname>Tahir</surname>
<given-names>MJ</given-names>
</name>
<name>
<surname>Mehmood</surname>
<given-names>Q</given-names>
</name>
<name>
<surname>Tabassum</surname>
<given-names>MS</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey</article-title>
<source>Ann Med Surg (Lond)</source>
<year iso-8601-date="2022">2022</year>
<volume>76</volume>
<elocation-id>103493</elocation-id>
<pub-id pub-id-type="doi">10.1016/j.amsu.2022.103493</pub-id>
<pub-id pub-id-type="pmid">35308436</pub-id>
<pub-id pub-id-type="pmcid">PMC8928127</pub-id>
</element-citation>
</ref>
<ref id="B46">
<label>46</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Jarab</surname>
<given-names>AS</given-names>
</name>
<name>
<surname>Abu</surname>
<given-names>Heshmeh SR</given-names>
</name>
<name>
<surname>Al</surname>
<given-names>Meslamani AZ</given-names>
</name>
</person-group>
<article-title>Artificial intelligence (AI) in pharmacy: an overview of innovations</article-title>
<source>J Med Econ</source>
<year iso-8601-date="2023">2023</year>
<volume>26</volume>
<fpage>1261</fpage>
<lpage>5</lpage>
<pub-id pub-id-type="doi">10.1080/13696998.2023.2265245</pub-id>
<pub-id pub-id-type="pmid">37772743</pub-id>
</element-citation>
</ref>
<ref id="B47">
<label>47</label>
<element-citation publication-type="journal">
<person-group person-group-type="author">
<name>
<surname>Jarab</surname>
<given-names>AS</given-names>
</name>
<name>
<surname>Al-Qerem</surname>
<given-names>W</given-names>
</name>
<name>
<surname>Alzoubi</surname>
<given-names>KH</given-names>
</name>
<name>
<surname>Obeidat</surname>
<given-names>H</given-names>
</name>
<name>
<surname>Abu</surname>
<given-names>Heshmeh S</given-names>
</name>
<name>
<surname>Mukattash</surname>
<given-names>TL</given-names>
</name>
<etal>et al.</etal>
</person-group>
<article-title>Artificial intelligence in pharmacy practice: Attitude and willingness of the community pharmacists and the barriers for its implementation</article-title>
<source>Saudi Pharm J</source>
<year iso-8601-date="2023">2023</year>
<volume>31</volume>
<elocation-id>101700</elocation-id>
<pub-id pub-id-type="doi">10.1016/j.jsps.2023.101700</pub-id>
<pub-id pub-id-type="pmid">37555012</pub-id>
<pub-id pub-id-type="pmcid">PMC10404546</pub-id>
</element-citation>
</ref>
</ref-list>
</back>
</article>