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Medicine is undergoing a deep technological transformation, with surgery on the cusp of this change, as technologies such as artificial intelligence (AI), augmented reality (AR), real-time imaging, and robotics converge to transform operative care. These innovations are now progressively being integrated into practice, driving precision surgery closer to reality. We aimed to assess how the convergence of AI, AR, real-time imaging, and robotics is advancing precision surgery and to outline the next wave of operative care. For doing this, we conducted a narrative perspective of publications that address AI-driven decision-making, AR-guided navigation, semi-autonomous robotics, and real-time imaging tailoring in surgical contexts. We observed that AI algorithms are expanding the potential of medicine by analyzing diverse data sets to optimize treatment strategies. AR-based navigation systems overlay digital anatomical information onto the surgical field, improving surgeon awareness and accuracy. Concurrently, AI-powered robotics are beginning to perform some surgical tasks semi-autonomously, potentially shortening procedure times and improving patient outcomes. Over time, the synergy between these disciplines may yield a new era of surgery: One where patient stratification guides operative decisions and where surgeons rely on data-driven systems for intraoperative feedback. This approach reinforces the principles of precision medicine and points toward a future in which surgery evolves hand in hand to improve clinical outcomes and patient safety. The synergy of data-driven surgery and personalized therapeutics brings a new era in precision medicine in which operative decisions may be dynamically tailored to the individual, promising greater safety and better clinical results.
Medicine is undergoing a deep technological transformation, with surgery on the cusp of this change, as technologies such as artificial intelligence (AI), augmented reality (AR), real-time imaging, and robotics converge to transform operative care. These innovations are now progressively being integrated into practice, driving precision surgery closer to reality. We aimed to assess how the convergence of AI, AR, real-time imaging, and robotics is advancing precision surgery and to outline the next wave of operative care. For doing this, we conducted a narrative perspective of publications that address AI-driven decision-making, AR-guided navigation, semi-autonomous robotics, and real-time imaging tailoring in surgical contexts. We observed that AI algorithms are expanding the potential of medicine by analyzing diverse data sets to optimize treatment strategies. AR-based navigation systems overlay digital anatomical information onto the surgical field, improving surgeon awareness and accuracy. Concurrently, AI-powered robotics are beginning to perform some surgical tasks semi-autonomously, potentially shortening procedure times and improving patient outcomes. Over time, the synergy between these disciplines may yield a new era of surgery: One where patient stratification guides operative decisions and where surgeons rely on data-driven systems for intraoperative feedback. This approach reinforces the principles of precision medicine and points toward a future in which surgery evolves hand in hand to improve clinical outcomes and patient safety. The synergy of data-driven surgery and personalized therapeutics brings a new era in precision medicine in which operative decisions may be dynamically tailored to the individual, promising greater safety and better clinical results.
DOI: https://doi.org/10.37349/edht.2025.101162
Digital mental health interventions (DMHIs) have demonstrated considerable potential to address mental health needs across diverse populations by offering scalable, adaptable, and cost-effective solutions. Nevertheless, their implementation in the Global South—particularly among Indigenous peoples and traditional communities—remains scattered and insufficiently systematised. These communities frequently face structural inequalities, limited access to formal mental health services, and distinct sociocultural frameworks that necessitate the development of culturally appropriate digital interventions. To date, existing reviews have focused predominantly on high-income countries, highlighting the urgent need to synthesise context-specific evidence from low- and middle-income settings. Therefore, this protocol outlines the design of a scoping review aimed at examining the available evidence on DMHIs targeting Indigenous peoples and traditional communities in the Global South. The scoping review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews, and the results will be reported in line with the PRISMA-ScR guidelines. It will include articles resulting from primary research, systematic reviews, and opinion papers related to DMHIs in Indigenous and traditional populations in the Global South. No restrictions will be applied regarding the languages and year of publication. The search will be conducted in the following databases: MEDLINE, CINAHL, PsycINFO, Scopus, Embase, BVS Lilacs, African Index Medicus, and Index Medicus for the South-East Asia Region. Study selection and data extraction will be performed independently by three reviewers. The synthesis will include a numerical summary to provide an overview of the characteristics of the included studies, as well as a qualitative analysis aimed at identifying, analysing, and reporting recurring patterns and emerging categories within the data. The results will inform the development of future culturally competent digital mental health strategies tailored to the needs of Indigenous and traditional communities in the Global South.
Digital mental health interventions (DMHIs) have demonstrated considerable potential to address mental health needs across diverse populations by offering scalable, adaptable, and cost-effective solutions. Nevertheless, their implementation in the Global South—particularly among Indigenous peoples and traditional communities—remains scattered and insufficiently systematised. These communities frequently face structural inequalities, limited access to formal mental health services, and distinct sociocultural frameworks that necessitate the development of culturally appropriate digital interventions. To date, existing reviews have focused predominantly on high-income countries, highlighting the urgent need to synthesise context-specific evidence from low- and middle-income settings. Therefore, this protocol outlines the design of a scoping review aimed at examining the available evidence on DMHIs targeting Indigenous peoples and traditional communities in the Global South. The scoping review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews, and the results will be reported in line with the PRISMA-ScR guidelines. It will include articles resulting from primary research, systematic reviews, and opinion papers related to DMHIs in Indigenous and traditional populations in the Global South. No restrictions will be applied regarding the languages and year of publication. The search will be conducted in the following databases: MEDLINE, CINAHL, PsycINFO, Scopus, Embase, BVS Lilacs, African Index Medicus, and Index Medicus for the South-East Asia Region. Study selection and data extraction will be performed independently by three reviewers. The synthesis will include a numerical summary to provide an overview of the characteristics of the included studies, as well as a qualitative analysis aimed at identifying, analysing, and reporting recurring patterns and emerging categories within the data. The results will inform the development of future culturally competent digital mental health strategies tailored to the needs of Indigenous and traditional communities in the Global South.
DOI: https://doi.org/10.37349/edht.2025.101161
This article belongs to the special issue Digital Health Innovations in the Battle Against Psychological Problems: Progress, Hurdles, and Prospects
Virtual reality (VR) and digital health technologies have shown increasing potential in addressing psychological challenges such as homesickness and emotional distress, yet the role of emotional bonds, particularly place attachment, in shaping the design and effectiveness of these interventions remains underexplored. This study conceptualizes the integration of place attachment theory into digital health interventions, especially those utilizing VR, and proposes a theoretical and practical framework for designing emotionally resonant virtual environments. Two interrelated conceptual models are introduced: the Virtual Place Attachment Development Model (VPADM), which outlines psychological, social, environmental, and cultural dimensions that contribute to emotional bonding with virtual spaces, and the Cultural Adaptation System for Virtual Environments (CASVE), which addresses cross-cultural adaptation processes through assessment, implementation, and evaluation. These frameworks illustrate how virtual place attachment can be purposefully designed to enhance user engagement and emotional well-being, while also highlighting practical challenges such as accessibility, digital literacy, and the need for culturally responsive content. By integrating place attachment theory into digital mental health design, the paper offers a pathway to improve therapeutic outcomes in VR environments and provides a foundation for researchers and practitioners to develop emotionally supportive, culturally meaningful, and context-sensitive digital health interventions.
Virtual reality (VR) and digital health technologies have shown increasing potential in addressing psychological challenges such as homesickness and emotional distress, yet the role of emotional bonds, particularly place attachment, in shaping the design and effectiveness of these interventions remains underexplored. This study conceptualizes the integration of place attachment theory into digital health interventions, especially those utilizing VR, and proposes a theoretical and practical framework for designing emotionally resonant virtual environments. Two interrelated conceptual models are introduced: the Virtual Place Attachment Development Model (VPADM), which outlines psychological, social, environmental, and cultural dimensions that contribute to emotional bonding with virtual spaces, and the Cultural Adaptation System for Virtual Environments (CASVE), which addresses cross-cultural adaptation processes through assessment, implementation, and evaluation. These frameworks illustrate how virtual place attachment can be purposefully designed to enhance user engagement and emotional well-being, while also highlighting practical challenges such as accessibility, digital literacy, and the need for culturally responsive content. By integrating place attachment theory into digital mental health design, the paper offers a pathway to improve therapeutic outcomes in VR environments and provides a foundation for researchers and practitioners to develop emotionally supportive, culturally meaningful, and context-sensitive digital health interventions.
DOI: https://doi.org/10.37349/edht.2025.101160
Aim:
Moving beyond the traditional use/non-use dichotomy, this study examines how variations in older adults’ internet use relate to their multidimensional frailty status.
Methods:
Data were drawn from the Belgian Ageing Studies (BAS), a large-scale cross-sectional survey conducted in Flanders (Belgium) and included 2,312 individuals aged 60 and older. Internet use was categorized into non-users, basic users, selective users and allround users. Multidimensional frailty was assessed using the Comprehensive Frailty Assessment Instrument (CFAI), covering physical, psychological, social and environmental domains. Multinomial logistic regression and Chi-squared automatic interaction detection (CHAID) were conducted.
Results:
Regression analysis revealed that older adults with mild or high levels of physical frailty, as well as those with high levels of environmental frailty, were more likely to not use the internet. Furthermore, individuals with high physical frailty and high social frailty were more likely to be basic internet users. Social frailty was also linked to allround internet use, with those in the mild and high frailty categories being less likely to be allround users. However, CHAID analysis highlighted that sociodemographic factors—particularly low education and advanced age—are more strongly associated with low internet usage than frailty itself.
Conclusions:
Multidimensional frailty is associated with internet use, with mild and high frailty groups being less internet savvy.
Aim:
Moving beyond the traditional use/non-use dichotomy, this study examines how variations in older adults’ internet use relate to their multidimensional frailty status.
Methods:
Data were drawn from the Belgian Ageing Studies (BAS), a large-scale cross-sectional survey conducted in Flanders (Belgium) and included 2,312 individuals aged 60 and older. Internet use was categorized into non-users, basic users, selective users and allround users. Multidimensional frailty was assessed using the Comprehensive Frailty Assessment Instrument (CFAI), covering physical, psychological, social and environmental domains. Multinomial logistic regression and Chi-squared automatic interaction detection (CHAID) were conducted.
Results:
Regression analysis revealed that older adults with mild or high levels of physical frailty, as well as those with high levels of environmental frailty, were more likely to not use the internet. Furthermore, individuals with high physical frailty and high social frailty were more likely to be basic internet users. Social frailty was also linked to allround internet use, with those in the mild and high frailty categories being less likely to be allround users. However, CHAID analysis highlighted that sociodemographic factors—particularly low education and advanced age—are more strongly associated with low internet usage than frailty itself.
Conclusions:
Multidimensional frailty is associated with internet use, with mild and high frailty groups being less internet savvy.
DOI: https://doi.org/10.37349/edht.2025.101159
Aim:
Colorectal cancer is a leading cause of cancer-related mortality, emphasising the need for accurate polyp segmentation during colonoscopy for early detection. Existing methods often struggle to generalize effectively across diverse clinical scenarios. This study introduces DeepPolyp, an artificial intelligence framework designed for comprehensive benchmarking and real-time clinical deployment of polyp segmentation models.
Methods:
Transformer-based segmentation models, SegFormer and SSFormer, were trained from scratch using an extensive dataset comprising public collections (CVC-ClinicDB, ETIS-LaribPolypDB, Kvasir) and recently augmented datasets (PolypDataset-TCNoEndo, PolypGen). Training involved standardized data augmentation, learning rate schedules, and early stopping. Models were evaluated using Dice and Intersection over Union (IoU) metrics. Real-time inference performance was assessed on an NVIDIA Jetson Orin device with ONNX and TensorRT optimizations.
Results:
SegFormer-B4 achieved the highest accuracy (Dice: 0.9843, IoU: 0.9694), but was not selected for clinical deployment due to computational constraints. SegFormer-B2 provided comparable accuracy (Dice: 0.9787, IoU: 0.9588) with significantly faster inference (94 ms per frame), offering an optimal balance suitable for real-time clinical use. SSFormer showed lower accuracy and slower inference, limiting its practical deployment.
Conclusions:
DeepPolyp enables systematic evaluation of polyp segmentation models, assisting in selecting models based on both performance and computational efficiency. Despite superior accuracy from SegFormer-B4, SegFormer-B2 was selected for clinical deployment due to its advantageous balance between accuracy and real-time execution efficiency.
Aim:
Colorectal cancer is a leading cause of cancer-related mortality, emphasising the need for accurate polyp segmentation during colonoscopy for early detection. Existing methods often struggle to generalize effectively across diverse clinical scenarios. This study introduces DeepPolyp, an artificial intelligence framework designed for comprehensive benchmarking and real-time clinical deployment of polyp segmentation models.
Methods:
Transformer-based segmentation models, SegFormer and SSFormer, were trained from scratch using an extensive dataset comprising public collections (CVC-ClinicDB, ETIS-LaribPolypDB, Kvasir) and recently augmented datasets (PolypDataset-TCNoEndo, PolypGen). Training involved standardized data augmentation, learning rate schedules, and early stopping. Models were evaluated using Dice and Intersection over Union (IoU) metrics. Real-time inference performance was assessed on an NVIDIA Jetson Orin device with ONNX and TensorRT optimizations.
Results:
SegFormer-B4 achieved the highest accuracy (Dice: 0.9843, IoU: 0.9694), but was not selected for clinical deployment due to computational constraints. SegFormer-B2 provided comparable accuracy (Dice: 0.9787, IoU: 0.9588) with significantly faster inference (94 ms per frame), offering an optimal balance suitable for real-time clinical use. SSFormer showed lower accuracy and slower inference, limiting its practical deployment.
Conclusions:
DeepPolyp enables systematic evaluation of polyp segmentation models, assisting in selecting models based on both performance and computational efficiency. Despite superior accuracy from SegFormer-B4, SegFormer-B2 was selected for clinical deployment due to its advantageous balance between accuracy and real-time execution efficiency.
DOI: https://doi.org/10.37349/edht.2025.101158
This article belongs to the special issue Deep Learning Methods and Applications for Biomedical Imaging
Aim:
Oxidative stress (OS) remains an intensively studied scientific problem. The quantitative measurement of OS is an unsolved task, largely due to the existence of numerous complex, non-linear interactions of its components, which can not be measured by traditional statistical methods. Modern mathematical processing based on artificial intelligence (AI) could be a promising method of OS assessment in medicine. The aim of the study was to investigate the potential possibilities of using multilayer neural networks to improve the diagnostic informativeness of the OS indicator—antioxidant (AO) activity (AOA) in patients with cardiovascular diseases (CVDs).
Methods:
A cross-sectional study of a sample of 856 people, healthy volunteers and several groups of patients with CVDs (hypertension, including those complicated by coronary heart disease and/or cerebral ischemia, chronic cerebral ischemia), was carried out. The potentiometric method of determining the OS indicator, index of blood serum AOA, was used in comparison with a number of laboratory tests and clinical data. After the results of linear statistical evaluations were not satisfactory enough, а multilayer perceptron classifier was constructed for data analysis.
Results:
By training a neural network, it was possible to assign a patient to one of the above-mentioned groups with 85% accuracy on the basis of 8 parameters selected from all the patients’ clinical and laboratory data, including the AOA value.
Conclusions:
The use of multilayer neural networks can improve the diagnostic value of information obtained during the measurement of AOA index, in combination with simple laboratory tests in patients with CVDs. The application of AI algorithms is a promising tool to improve the laboratory measurement of OS and a potential solution to overcome the contradictions in the existing approaches to the evaluation of OS.
Aim:
Oxidative stress (OS) remains an intensively studied scientific problem. The quantitative measurement of OS is an unsolved task, largely due to the existence of numerous complex, non-linear interactions of its components, which can not be measured by traditional statistical methods. Modern mathematical processing based on artificial intelligence (AI) could be a promising method of OS assessment in medicine. The aim of the study was to investigate the potential possibilities of using multilayer neural networks to improve the diagnostic informativeness of the OS indicator—antioxidant (AO) activity (AOA) in patients with cardiovascular diseases (CVDs).
Methods:
A cross-sectional study of a sample of 856 people, healthy volunteers and several groups of patients with CVDs (hypertension, including those complicated by coronary heart disease and/or cerebral ischemia, chronic cerebral ischemia), was carried out. The potentiometric method of determining the OS indicator, index of blood serum AOA, was used in comparison with a number of laboratory tests and clinical data. After the results of linear statistical evaluations were not satisfactory enough, а multilayer perceptron classifier was constructed for data analysis.
Results:
By training a neural network, it was possible to assign a patient to one of the above-mentioned groups with 85% accuracy on the basis of 8 parameters selected from all the patients’ clinical and laboratory data, including the AOA value.
Conclusions:
The use of multilayer neural networks can improve the diagnostic value of information obtained during the measurement of AOA index, in combination with simple laboratory tests in patients with CVDs. The application of AI algorithms is a promising tool to improve the laboratory measurement of OS and a potential solution to overcome the contradictions in the existing approaches to the evaluation of OS.
DOI: https://doi.org/10.37349/edht.2025.101157
Aim:
Structured light plethysmography (SLP), is a contactless optical system developed to monitor breathing patterns by analyzing chest-wall movement. It has not been thoroughly validated against other non-invasive motion analysis systems under different breathing conditions. This study therefore aimed to evaluate the criterion-validity of the SLP compared to the respiratory inductive plethysmography (RIP) at rest and after exercise.
Methods:
Adults underwent two simultaneous 5-minute recordings from both devices, conducted at rest and following submaximal exercise on a cycle ergometer. Timing indices and thoracoabdominal (TA) movement parameters were examined. Measurement agreement between SLP and RIP was assessed using Bland-Altman plots at rest, after exercise, and for exercise-induced changes.
Results:
Fifty adults (mean age 29.3 ± 6.8 years; 30 males) participated. Α total of 3,395 and 4,295 breath cycles were analyzed at rest and post-exercise, respectively. Over 92% of differences in timing parameters under both conditions were within the 95% limits of agreement (LOA) and their mean differences were found close to zero across a wide range of breath cycle magnitudes (rest: 2.62–8.06 s; post-exercise: 2.16–6.16 s). For ΤΑ movement parameters, the mean bias between devices at rest was 0.31 for ribcage amplitude (RCampi) and 0.23 for abdominal amplitude (ABampi), with LOA ranging from −0.06 to 0.66 and −0.06 to 0.52, respectively. A trend towards greater discrepancies for the individual measurements of RCampi and ABampi at higher magnitudes of TA movements was noted, especially post-exercise. A good average agreement between the devices was found for RCampi/ABampi both at rest [mean difference: 0.03, standard deviation (SD): 0.21] and after exercise (mean difference: 1.10, SD: 0.24).
Conclusions:
The SLP is an accurate method to quantify and measure timing indices and the ratio of the ribcage motion to the abdominal motion under different breathing conditions.
Aim:
Structured light plethysmography (SLP), is a contactless optical system developed to monitor breathing patterns by analyzing chest-wall movement. It has not been thoroughly validated against other non-invasive motion analysis systems under different breathing conditions. This study therefore aimed to evaluate the criterion-validity of the SLP compared to the respiratory inductive plethysmography (RIP) at rest and after exercise.
Methods:
Adults underwent two simultaneous 5-minute recordings from both devices, conducted at rest and following submaximal exercise on a cycle ergometer. Timing indices and thoracoabdominal (TA) movement parameters were examined. Measurement agreement between SLP and RIP was assessed using Bland-Altman plots at rest, after exercise, and for exercise-induced changes.
Results:
Fifty adults (mean age 29.3 ± 6.8 years; 30 males) participated. Α total of 3,395 and 4,295 breath cycles were analyzed at rest and post-exercise, respectively. Over 92% of differences in timing parameters under both conditions were within the 95% limits of agreement (LOA) and their mean differences were found close to zero across a wide range of breath cycle magnitudes (rest: 2.62–8.06 s; post-exercise: 2.16–6.16 s). For ΤΑ movement parameters, the mean bias between devices at rest was 0.31 for ribcage amplitude (RCampi) and 0.23 for abdominal amplitude (ABampi), with LOA ranging from −0.06 to 0.66 and −0.06 to 0.52, respectively. A trend towards greater discrepancies for the individual measurements of RCampi and ABampi at higher magnitudes of TA movements was noted, especially post-exercise. A good average agreement between the devices was found for RCampi/ABampi both at rest [mean difference: 0.03, standard deviation (SD): 0.21] and after exercise (mean difference: 1.10, SD: 0.24).
Conclusions:
The SLP is an accurate method to quantify and measure timing indices and the ratio of the ribcage motion to the abdominal motion under different breathing conditions.
DOI: https://doi.org/10.37349/edht.2025.101156
Aim:
This study aimed to evaluate virtual reality restorative environments (VRREs)’ impact on university students’ mental well-being, investigate factors affecting VRRE perception and psychological recovery, understand virtual environments’ healing mechanisms, and provide recommendations for university virtual healing spaces.
Methods:
Semi-structured interviews were conducted with 32 participants to develop a VRRE perception evaluation system with five core and fourteen main categories. The system was then used to assess 13 virtual natural environments. Mental recovery effects were measured among 44 university students using the Schulte test (attention), Positive and Negative Affection Scale (mood), and physiological sensors (stress).
Results:
VRREs demonstrated significant positive effects on participants’ psychological recovery. Different virtual environments showed varying impacts on attention, negative affect, and stress levels, while effects on positive affect were consistent across environments. Virtual extraterrestrial space environments yielded the strongest improvements in attention and stress reduction, whereas mixed forest settings were most effective in decreasing negative affects. Structural equation modeling revealed that participants’ VRRE perceptions significantly influenced psychological recovery through seven of fifteen pathways.
Conclusions:
VRREs represent an effective intervention for supporting university students’ mental well-being. Different virtual environments offer distinct psychological benefits, with environment perception playing a crucial role in recovery outcomes. These findings provide valuable insights for designing targeted virtual healing spaces in university settings.
Aim:
This study aimed to evaluate virtual reality restorative environments (VRREs)’ impact on university students’ mental well-being, investigate factors affecting VRRE perception and psychological recovery, understand virtual environments’ healing mechanisms, and provide recommendations for university virtual healing spaces.
Methods:
Semi-structured interviews were conducted with 32 participants to develop a VRRE perception evaluation system with five core and fourteen main categories. The system was then used to assess 13 virtual natural environments. Mental recovery effects were measured among 44 university students using the Schulte test (attention), Positive and Negative Affection Scale (mood), and physiological sensors (stress).
Results:
VRREs demonstrated significant positive effects on participants’ psychological recovery. Different virtual environments showed varying impacts on attention, negative affect, and stress levels, while effects on positive affect were consistent across environments. Virtual extraterrestrial space environments yielded the strongest improvements in attention and stress reduction, whereas mixed forest settings were most effective in decreasing negative affects. Structural equation modeling revealed that participants’ VRRE perceptions significantly influenced psychological recovery through seven of fifteen pathways.
Conclusions:
VRREs represent an effective intervention for supporting university students’ mental well-being. Different virtual environments offer distinct psychological benefits, with environment perception playing a crucial role in recovery outcomes. These findings provide valuable insights for designing targeted virtual healing spaces in university settings.
DOI: https://doi.org/10.37349/edht.2025.101155
Aim:
The spread of suicide and non-suicidal self-injury (NSSI) content on social media has raised ongoing concerns about user safety and mental health. In response, social media platforms like Twitter (now X) and Meta (i.e., Facebook and Instagram) introduced content moderation policies to mitigate harm and promote safer digital environments. This study explored immediate trends in user discourse surrounding suicide and NSSI following the enactment of Meta’s graphic self-harm imagery ban. Specifically, it examined shifts in tweet tone, content type, and underlying themes immediately before and after the policy’s implementation.
Methods:
A corpus of 3,846 tweets was analyzed. Within this corpus, tweets spanning 32 weeks from October 18, 2018, to May 29, 2019, were selected. These dates were chosen to encompass approximately 16 weeks before and after the enactment of the policy on February 7, 2019. Tweets were categorized according to slant, tweet category, and theme.
Results:
The findings revealed notable shifts in online discourse. There was a significant decrease in the proportion of tweets identified as anti-self-harm tweets and a corresponding increase in the proportion of tweets aimed at understanding self-harm, many of which were coded as personal opinions or informative content. These trends suggest that while content promoting self-harm did not increase, the tone of discourse shifted toward greater nuance and reflection. This may reflect users’ growing efforts to process, contextualize, and share perspectives on self-harm in a policy-regulated environment.
Conclusions:
Meta’s graphic self-harm imagery ban appeared to influence how users communicated about suicide and NSSI on Twitter, prompting more content centered on understanding and discussion. However, the findings also highlight challenges in balancing harm reduction with space for personal narratives. These insights emphasize the role of policy in shaping public discourse and the need for clear moderation strategies that distinguish harmful promotion from lived experience and peer support.
Aim:
The spread of suicide and non-suicidal self-injury (NSSI) content on social media has raised ongoing concerns about user safety and mental health. In response, social media platforms like Twitter (now X) and Meta (i.e., Facebook and Instagram) introduced content moderation policies to mitigate harm and promote safer digital environments. This study explored immediate trends in user discourse surrounding suicide and NSSI following the enactment of Meta’s graphic self-harm imagery ban. Specifically, it examined shifts in tweet tone, content type, and underlying themes immediately before and after the policy’s implementation.
Methods:
A corpus of 3,846 tweets was analyzed. Within this corpus, tweets spanning 32 weeks from October 18, 2018, to May 29, 2019, were selected. These dates were chosen to encompass approximately 16 weeks before and after the enactment of the policy on February 7, 2019. Tweets were categorized according to slant, tweet category, and theme.
Results:
The findings revealed notable shifts in online discourse. There was a significant decrease in the proportion of tweets identified as anti-self-harm tweets and a corresponding increase in the proportion of tweets aimed at understanding self-harm, many of which were coded as personal opinions or informative content. These trends suggest that while content promoting self-harm did not increase, the tone of discourse shifted toward greater nuance and reflection. This may reflect users’ growing efforts to process, contextualize, and share perspectives on self-harm in a policy-regulated environment.
Conclusions:
Meta’s graphic self-harm imagery ban appeared to influence how users communicated about suicide and NSSI on Twitter, prompting more content centered on understanding and discussion. However, the findings also highlight challenges in balancing harm reduction with space for personal narratives. These insights emphasize the role of policy in shaping public discourse and the need for clear moderation strategies that distinguish harmful promotion from lived experience and peer support.
DOI: https://doi.org/10.37349/edht.2025.101154
This article belongs to the special issue Digital Health Innovations in the Battle Against Psychological Problems: Progress, Hurdles, and Prospects
Over the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted immunotherapy has improved overall survival and treatment response. However, immunotherapy is expensive, and only select patients respond to immunotherapy. Recently, there has been much interest in using biomarkers to better identify and predict which patients will respond to therapy. There is much hope that the combined use of artificial intelligence (AI) and omics-based technology will provide enhanced capability to predict response to immunotherapy in patients with NSCLC. We performed a literature review and summarized the various approaches in which AI has been integrated with genomics, radiomics, pathomics, metabolomics, immunogenomics, and breathomics to better understand the tumor immune microenvironment and predict response to immunotherapy.
Over the last four decades, lung cancer has been the leading cause of death in the United States. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and historically, treatment consists of surgical resection, chemotherapy, and/or radiotherapy. Over the past decade, targeted immunotherapy has improved overall survival and treatment response. However, immunotherapy is expensive, and only select patients respond to immunotherapy. Recently, there has been much interest in using biomarkers to better identify and predict which patients will respond to therapy. There is much hope that the combined use of artificial intelligence (AI) and omics-based technology will provide enhanced capability to predict response to immunotherapy in patients with NSCLC. We performed a literature review and summarized the various approaches in which AI has been integrated with genomics, radiomics, pathomics, metabolomics, immunogenomics, and breathomics to better understand the tumor immune microenvironment and predict response to immunotherapy.
DOI: https://doi.org/10.37349/edht.2025.101153
This article belongs to the special issue Cancer Diagnosis in the Digital Age
Aim:
To explore patient satisfaction with telemedicine and its associated factors at the Rheumatology Outpatient Clinic, San Fernando Teaching Hospital (SFTH), and to determine patient preference for health-related consultations.
Methods:
305 patients were surveyed via consecutive sampling. Data was obtained via interviewer-administered questionnaires in a clinical setting, capturing demographics, challenges with face-to-face consultations, and patient perspectives on telemedicine. Items from the Telemedicine Satisfaction Questionnaire and Telehealth Usability Questionnaire were modified to capture impact. Data was analyzed using descriptive and inferential statistics (SPSS version 29).
Results:
Most respondents were ≥ 40 years old (77.7%), Indo-Caribbean (66.2%), female (89.2%), unemployed (64.9%), and had secondary level education or higher (76.1%). Time off issues (13.0%), timing inconvenience (12.4%), and traveling costs (12.4%) were identified as challenges with face-to-face consultations. Fear of interaction (22.9%) and financial difficulty (22.7%), widely resulting from COVID-19, were additional challenges. Most patients reported satisfaction with telemedicine (71.5%), relating to easier access to health services (65.9%). Combined telemedicine and face-to-face consultations, as appropriate, were the most preferred option (73.4%). Several socio-demographic factors influenced patient satisfaction and preference for telemedicine services, with telemedicine convenience being the most significant factor.
Conclusions:
The results conclude that patients at the Rheumatology Outpatient Clinic are satisfied with the current telemedicine service as a method of providing continuity of care (p < 0.001). Challenges encountered with face-to-face consultations and the COVID-19 pandemic can influence patients’ level of satisfaction with and preference for telemedicine. Telemedicine convenience was the most significant factor influencing patient satisfaction and preference (p < 0.001). Most patients’ preference for a combination approach of both telemedicine and face-to-face consultations reflects the current standard of care. The findings of this study suggest that telemedicine is reasonable to incorporate into outpatient care for patients with chronic rheumatological diseases.
Aim:
To explore patient satisfaction with telemedicine and its associated factors at the Rheumatology Outpatient Clinic, San Fernando Teaching Hospital (SFTH), and to determine patient preference for health-related consultations.
Methods:
305 patients were surveyed via consecutive sampling. Data was obtained via interviewer-administered questionnaires in a clinical setting, capturing demographics, challenges with face-to-face consultations, and patient perspectives on telemedicine. Items from the Telemedicine Satisfaction Questionnaire and Telehealth Usability Questionnaire were modified to capture impact. Data was analyzed using descriptive and inferential statistics (SPSS version 29).
Results:
Most respondents were ≥ 40 years old (77.7%), Indo-Caribbean (66.2%), female (89.2%), unemployed (64.9%), and had secondary level education or higher (76.1%). Time off issues (13.0%), timing inconvenience (12.4%), and traveling costs (12.4%) were identified as challenges with face-to-face consultations. Fear of interaction (22.9%) and financial difficulty (22.7%), widely resulting from COVID-19, were additional challenges. Most patients reported satisfaction with telemedicine (71.5%), relating to easier access to health services (65.9%). Combined telemedicine and face-to-face consultations, as appropriate, were the most preferred option (73.4%). Several socio-demographic factors influenced patient satisfaction and preference for telemedicine services, with telemedicine convenience being the most significant factor.
Conclusions:
The results conclude that patients at the Rheumatology Outpatient Clinic are satisfied with the current telemedicine service as a method of providing continuity of care (p < 0.001). Challenges encountered with face-to-face consultations and the COVID-19 pandemic can influence patients’ level of satisfaction with and preference for telemedicine. Telemedicine convenience was the most significant factor influencing patient satisfaction and preference (p < 0.001). Most patients’ preference for a combination approach of both telemedicine and face-to-face consultations reflects the current standard of care. The findings of this study suggest that telemedicine is reasonable to incorporate into outpatient care for patients with chronic rheumatological diseases.
DOI: https://doi.org/10.37349/edht.2025.101152
Spatial memory, a fundamental cognitive function, enables individuals to encode, store, and retrieve information about their surroundings. Traditional assessment methods, such as paper-based tests and laboratory paradigms, often lack ecological validity and fail to capture the complexities of real-world navigation. Recent advancements in digital technologies, particularly virtual reality (VR) and mixed reality (MR), have introduced innovative tools for more immersive and accurate spatial memory assessments. VR provides controlled, replicable environments that simulate real-world navigation, while MR enhances engagement by blending virtual elements with physical spaces. This narrative review explores the cognitive mechanisms underlying spatial memory, highlighting the roles of egocentric and allocentric reference frames, as well as the neural substrates involved. The review also examines key factors influencing spatial memory performance, such as age, sex, neurological and neurodegenerative diseases. Digital tools such as the virtual Morris water maze and the VR Supermarket Test have been shown to possess enhanced ecological validity and diagnostic potential, particularly in the context of detecting early cognitive decline in Alzheimer’s disease. However, the field confronts several challenges, including the necessity for standardized protocols, the potential for adverse effects such as cybersickness, and the substantial cost associated with VR and MR systems. Future research directions in this field should include the integration of artificial intelligence for personalized assessments, and the combination of VR and MR tasks with neurophysiological techniques to advance understanding of spatial memory. Standardization, accessibility, and the creation of adaptive assessment for clinical populations will be crucial for optimizing the use of digital technologies in spatial memory research.
Spatial memory, a fundamental cognitive function, enables individuals to encode, store, and retrieve information about their surroundings. Traditional assessment methods, such as paper-based tests and laboratory paradigms, often lack ecological validity and fail to capture the complexities of real-world navigation. Recent advancements in digital technologies, particularly virtual reality (VR) and mixed reality (MR), have introduced innovative tools for more immersive and accurate spatial memory assessments. VR provides controlled, replicable environments that simulate real-world navigation, while MR enhances engagement by blending virtual elements with physical spaces. This narrative review explores the cognitive mechanisms underlying spatial memory, highlighting the roles of egocentric and allocentric reference frames, as well as the neural substrates involved. The review also examines key factors influencing spatial memory performance, such as age, sex, neurological and neurodegenerative diseases. Digital tools such as the virtual Morris water maze and the VR Supermarket Test have been shown to possess enhanced ecological validity and diagnostic potential, particularly in the context of detecting early cognitive decline in Alzheimer’s disease. However, the field confronts several challenges, including the necessity for standardized protocols, the potential for adverse effects such as cybersickness, and the substantial cost associated with VR and MR systems. Future research directions in this field should include the integration of artificial intelligence for personalized assessments, and the combination of VR and MR tasks with neurophysiological techniques to advance understanding of spatial memory. Standardization, accessibility, and the creation of adaptive assessment for clinical populations will be crucial for optimizing the use of digital technologies in spatial memory research.
DOI: https://doi.org/10.37349/edht.2025.101151
This article belongs to the special issue Digital Health Innovations in the Battle Against Psychological Problems: Progress, Hurdles, and Prospects
The enormous global burden of cancer has created the need to develop cutting-edge strategies for enhancing public education on cancer. Over the years, conventional educational strategies, such as the use of posters and leaflets, have been preferentially employed as public education strategies on oral cancer; however, the use of digital education-based strategies has been largely underutilized. Notably, the use of digital education-based strategies, particularly serious games, has proven to be a superior cancer education strategy, when compared to conventional strategies, due to their rigorous design and features. This commentary discusses serious games as a game changer in cancer education, itemizing their diverse roles in cancer prevention, advocacy, and management. Also, this commentary also detailed those factors that might limit the use and availability of serious games in resource-limited settings.
The enormous global burden of cancer has created the need to develop cutting-edge strategies for enhancing public education on cancer. Over the years, conventional educational strategies, such as the use of posters and leaflets, have been preferentially employed as public education strategies on oral cancer; however, the use of digital education-based strategies has been largely underutilized. Notably, the use of digital education-based strategies, particularly serious games, has proven to be a superior cancer education strategy, when compared to conventional strategies, due to their rigorous design and features. This commentary discusses serious games as a game changer in cancer education, itemizing their diverse roles in cancer prevention, advocacy, and management. Also, this commentary also detailed those factors that might limit the use and availability of serious games in resource-limited settings.
DOI: https://doi.org/10.37349/edht.2025.101150
Background:
The main theme of research literature on burnout has yet to be investigated. Aims: This bibliometric study evaluated the research literature on burnout and health, indexed in Web of Science (WoS), to reveal its expansion and the most prolific authors, institutions, countries, journals, and journal categories. The recurring themes of the literature were also identified.
Methods:
In December 2023, the WoS Core Collection database was queried with: TS = [(“burnout*” OR “burn out*” OR “burn-out*”) AND (“health*” OR “illness*” OR “disease*” OR “well-being*” OR “wellbeing*”)]. The search yielded publications with these words presented in their title, abstract, or keywords. No filter was placed to restrict the search. Publication and citation counts were recorded directly from the database, whereas subsequent analyses were performed with VOSviewer.
Results:
The search yielded 26,492 publications. The literature has been growing steadily in the 2000s and more quickly in the 2010s. Nearly one-third of the publications had contributions from the United States. The most prolific journals involved some open-access mega-journals and journals from psychology, medicine, and nursing. Depression and anxiety associated with burnout were recurring themes in the literature. The research community has been explaining burnout by the highly cited Job Demands-Resources (JD-R) model.
Discussion:
This work demonstrated the usefulness of a bibliometric analysis to identify key stakeholders and major themes of burnout research.
Background:
The main theme of research literature on burnout has yet to be investigated. Aims: This bibliometric study evaluated the research literature on burnout and health, indexed in Web of Science (WoS), to reveal its expansion and the most prolific authors, institutions, countries, journals, and journal categories. The recurring themes of the literature were also identified.
Methods:
In December 2023, the WoS Core Collection database was queried with: TS = [(“burnout*” OR “burn out*” OR “burn-out*”) AND (“health*” OR “illness*” OR “disease*” OR “well-being*” OR “wellbeing*”)]. The search yielded publications with these words presented in their title, abstract, or keywords. No filter was placed to restrict the search. Publication and citation counts were recorded directly from the database, whereas subsequent analyses were performed with VOSviewer.
Results:
The search yielded 26,492 publications. The literature has been growing steadily in the 2000s and more quickly in the 2010s. Nearly one-third of the publications had contributions from the United States. The most prolific journals involved some open-access mega-journals and journals from psychology, medicine, and nursing. Depression and anxiety associated with burnout were recurring themes in the literature. The research community has been explaining burnout by the highly cited Job Demands-Resources (JD-R) model.
Discussion:
This work demonstrated the usefulness of a bibliometric analysis to identify key stakeholders and major themes of burnout research.
DOI: https://doi.org/10.37349/edht.2025.101149
In recent years, patient engagement has emerged as a cornerstone in clinical decision-making, medical research, and health policy development, with its multifaceted value widely recognized by stakeholders across the healthcare continuum. However, digital health technologies, which are designed to enhance patient engagement, often fall short of their full potential due to developers’ limited understanding of patients’ needs and preferences. This perspective paper argues for adopting a patient-centered approach, emphasizing the critical importance of developers immersing themselves in patient communities to gain richer insights into patients’ lived experiences. Such an approach can lead to improved usability of digital health tools, enhanced user experience, and increased patient motivation, ultimately fostering more effective patient engagement in medical practice. Although challenges persist in the effective collection, analysis, and implementation of user feedback, prioritizing patient engagement remains crucial for optimizing health outcomes and enhancing the overall patient experience. By embracing this approach, developers can bridge the gap between technological innovation and patient needs, promoting more meaningful interactions and ultimately contributing to the advancement of healthcare systems and improved population health.
In recent years, patient engagement has emerged as a cornerstone in clinical decision-making, medical research, and health policy development, with its multifaceted value widely recognized by stakeholders across the healthcare continuum. However, digital health technologies, which are designed to enhance patient engagement, often fall short of their full potential due to developers’ limited understanding of patients’ needs and preferences. This perspective paper argues for adopting a patient-centered approach, emphasizing the critical importance of developers immersing themselves in patient communities to gain richer insights into patients’ lived experiences. Such an approach can lead to improved usability of digital health tools, enhanced user experience, and increased patient motivation, ultimately fostering more effective patient engagement in medical practice. Although challenges persist in the effective collection, analysis, and implementation of user feedback, prioritizing patient engagement remains crucial for optimizing health outcomes and enhancing the overall patient experience. By embracing this approach, developers can bridge the gap between technological innovation and patient needs, promoting more meaningful interactions and ultimately contributing to the advancement of healthcare systems and improved population health.
DOI: https://doi.org/10.37349/edht.2025.101148
This narrative review aims to appraise the evidence on artificial intelligence models for early diagnosis and risk stratification of oral cancer, focusing on data modalities, methodology differences, applications in the diagnostic flow and models’ performance. Models for early diagnosis and screening provide non-invasive diagnosis without the need for specialized instruments, which is ideal for early detection as a low-cost system. Supervised learning with well-annotated data provides reliable references for training the models, and therefore, reliable and promising results. Risk prediction models can be built based on medical record data, demographic data, clinical/histopathological descriptors, highly standardized images or a combination of these. Insights on which patients have a greater chance of malignancy development or disease recurrence can aid in providing personalized care, which can improve the patient’s prognosis. Artificial intelligence models demonstrate promising results in early diagnosis and risk stratification of oral cancer.
This narrative review aims to appraise the evidence on artificial intelligence models for early diagnosis and risk stratification of oral cancer, focusing on data modalities, methodology differences, applications in the diagnostic flow and models’ performance. Models for early diagnosis and screening provide non-invasive diagnosis without the need for specialized instruments, which is ideal for early detection as a low-cost system. Supervised learning with well-annotated data provides reliable references for training the models, and therefore, reliable and promising results. Risk prediction models can be built based on medical record data, demographic data, clinical/histopathological descriptors, highly standardized images or a combination of these. Insights on which patients have a greater chance of malignancy development or disease recurrence can aid in providing personalized care, which can improve the patient’s prognosis. Artificial intelligence models demonstrate promising results in early diagnosis and risk stratification of oral cancer.
DOI: https://doi.org/10.37349/edht.2025.101147
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer
Aim:
This study aimed to understand the mediating and moderating effects of self-esteem’s relationship with gaming disorder (GD).
Methods:
Participants (N = 1,712) were recruited from online gaming forums. A battery of measures including GD, self-esteem, depression, anxiety, escapism, and playing time were completed.
Results:
Escapism, depression, and playing time have a significant mediating effect on self-esteem’s relationship with GD. Escapism and depression explained most of the mediated effect, with playing time showing a much smaller effect. Anxiety was not a significant mediator. Unexpectedly, high self-esteem does not appear to buffer against the effects escapism and playing time have on GD. This contradicts clinical literature that promotes high self-esteem as a resilience factor.
Conclusions:
Mediating effects of self-esteem’s relationship with GD were identified in this study. Moderators other than self-esteem might be more prudent to investigate in GD research.
Aim:
This study aimed to understand the mediating and moderating effects of self-esteem’s relationship with gaming disorder (GD).
Methods:
Participants (N = 1,712) were recruited from online gaming forums. A battery of measures including GD, self-esteem, depression, anxiety, escapism, and playing time were completed.
Results:
Escapism, depression, and playing time have a significant mediating effect on self-esteem’s relationship with GD. Escapism and depression explained most of the mediated effect, with playing time showing a much smaller effect. Anxiety was not a significant mediator. Unexpectedly, high self-esteem does not appear to buffer against the effects escapism and playing time have on GD. This contradicts clinical literature that promotes high self-esteem as a resilience factor.
Conclusions:
Mediating effects of self-esteem’s relationship with GD were identified in this study. Moderators other than self-esteem might be more prudent to investigate in GD research.
DOI: https://doi.org/10.37349/edht.2025.101146
This article belongs to the special issue Digital Health Innovations in the Battle Against Psychological Problems: Progress, Hurdles, and Prospects
Background:
The use of artificial intelligence (AI) has been shown to enhance human life quality by making it easier, safer, and more efficient. However, there is currently limited evidence about the applicability of AI in health insurance and easing the complexity of insurance operations. This study seeks to systematically review the literature related to the application, challenges, and opportunities of applying AI in the healthcare insurance industry.
Methods:
A systematic review approach was utilized, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The method included an exploratory and narrative design, a two-phase search strategy, eligibility criteria, and analysis.
Results:
The search yielded 520 eligible articles. Twelve articles were eligible, evaluated, and analyzed in this study. Most articles discussed AI’s use in healthcare insurance to detect fraud, improve underwriting accuracy and transparency, and resolve medical information asymmetry. For claim processes, virtual agents, chatbots, customer engagement, telematics, and underwriting, algorithms were essential. However, technical skill is needed to create and deploy AI systems, and privacy was an issue due to massive data and algorithms that could abuse user data.
Discussion:
The implementation of AI encounters various challenges, such as insufficient knowledge among users, a deficit in technical expertise and support, shortcomings in data strategy, and a growing reluctance towards AI. Privacy presents a challenge in AI, especially because of the widespread use of large data sets and algorithms that could misuse consumer information.
Background:
The use of artificial intelligence (AI) has been shown to enhance human life quality by making it easier, safer, and more efficient. However, there is currently limited evidence about the applicability of AI in health insurance and easing the complexity of insurance operations. This study seeks to systematically review the literature related to the application, challenges, and opportunities of applying AI in the healthcare insurance industry.
Methods:
A systematic review approach was utilized, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The method included an exploratory and narrative design, a two-phase search strategy, eligibility criteria, and analysis.
Results:
The search yielded 520 eligible articles. Twelve articles were eligible, evaluated, and analyzed in this study. Most articles discussed AI’s use in healthcare insurance to detect fraud, improve underwriting accuracy and transparency, and resolve medical information asymmetry. For claim processes, virtual agents, chatbots, customer engagement, telematics, and underwriting, algorithms were essential. However, technical skill is needed to create and deploy AI systems, and privacy was an issue due to massive data and algorithms that could abuse user data.
Discussion:
The implementation of AI encounters various challenges, such as insufficient knowledge among users, a deficit in technical expertise and support, shortcomings in data strategy, and a growing reluctance towards AI. Privacy presents a challenge in AI, especially because of the widespread use of large data sets and algorithms that could misuse consumer information.
DOI: https://doi.org/10.37349/edht.2025.101145
Artificial intelligence (AI) increasingly influences biomedical scientific writing and clinical practice. The recent article by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024) explores AI’s capabilities, challenges, and ethical considerations in scientific communication, particularly highlighting tools like ChatGPT and Penelope.ai. This commentary aims to reflect on and expand the key themes presented by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024), emphasizing AI’s role in auditory healthcare, particularly in otolaryngology and auditory rehabilitation. The discussion is based on a critical review and synthesis of recent literature on AI applications in scientific writing and auditory healthcare. Key technologies such as generative AI platforms, machine learning algorithms, and mobile-based auditory training systems are highlighted. AI has shown promising results in enhancing manuscript preparation, literature synthesis, and peer review workflows. In clinical practice, adaptive AI models have improved cochlear implant programming, leading to up to 30% gains in speech perception accuracy. Mobile apps and telehealth platforms using AI have also improved listening effort, communication confidence, and access to care in remote settings. However, limitations include data privacy concerns, lack of population diversity in datasets, and the need for clinician oversight. AI presents transformative opportunities across biomedical science and healthcare. To ensure its responsible use, interdisciplinary collaboration among clinicians, researchers, ethicists, and technologists is essential. Such collaboration can help develop ethical frameworks that enhance innovation while safeguarding patient well-being and scientific integrity.
Artificial intelligence (AI) increasingly influences biomedical scientific writing and clinical practice. The recent article by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024) explores AI’s capabilities, challenges, and ethical considerations in scientific communication, particularly highlighting tools like ChatGPT and Penelope.ai. This commentary aims to reflect on and expand the key themes presented by Fornalik et al. (Explor Digit Health Technol. 2024;2:235–48. doi: 10.37349/edht.2024.00024), emphasizing AI’s role in auditory healthcare, particularly in otolaryngology and auditory rehabilitation. The discussion is based on a critical review and synthesis of recent literature on AI applications in scientific writing and auditory healthcare. Key technologies such as generative AI platforms, machine learning algorithms, and mobile-based auditory training systems are highlighted. AI has shown promising results in enhancing manuscript preparation, literature synthesis, and peer review workflows. In clinical practice, adaptive AI models have improved cochlear implant programming, leading to up to 30% gains in speech perception accuracy. Mobile apps and telehealth platforms using AI have also improved listening effort, communication confidence, and access to care in remote settings. However, limitations include data privacy concerns, lack of population diversity in datasets, and the need for clinician oversight. AI presents transformative opportunities across biomedical science and healthcare. To ensure its responsible use, interdisciplinary collaboration among clinicians, researchers, ethicists, and technologists is essential. Such collaboration can help develop ethical frameworks that enhance innovation while safeguarding patient well-being and scientific integrity.
DOI: https://doi.org/10.37349/edht.2025.101144
Mental healthcare in a range of countries faces challenges, including rapidly increasing demand at a time of restricted access to services, insufficient mental healthcare professionals and limited funding. This can result in long delays and late diagnosis. The use of artificial intelligence (AI) technology to help to address these shortcomings is therefore being explored in a range of countries, including the UK. The recent increase in reported studies provides an opportunity to review the potential, benefits and drawbacks of this technology. Studies have included AI-based chatbots for patients with depression and anxiety symptoms; AI-facilitated approaches, including virtual reality applications in anxiety disorders; avatar therapy for patients with psychosis; AI humanoid robot-enhanced therapy, for both children and the isolated elderly in care settings; AI animal-like robots to help patients with dementia; and digital game interventions for young people with mental health conditions. Overall, the studies showed positive effects and none reported any adverse side effects. However, the quality of the data was low, mainly due to a lack of studies, a high risk of bias and the heterogeneity of the studies. Importantly also, longer-term effects were often not evident. This suggests that translating small-scale, short-term trials into effective large-scale, longer-term real-world applications may be a particular challenge. While the use of AI in mental healthcare appears to have potential, its use also raises important ethical and privacy concerns, potential risk of bias, and the risk of unintended consequences such as over-diagnosis or unnecessary treatment of normal emotional experiences. More robust, longer-term research with larger patient populations, and clear regulatory frameworks and ethical guidelines to ensure that patients’ rights, privacy and well-being are protected, are therefore needed.
Mental healthcare in a range of countries faces challenges, including rapidly increasing demand at a time of restricted access to services, insufficient mental healthcare professionals and limited funding. This can result in long delays and late diagnosis. The use of artificial intelligence (AI) technology to help to address these shortcomings is therefore being explored in a range of countries, including the UK. The recent increase in reported studies provides an opportunity to review the potential, benefits and drawbacks of this technology. Studies have included AI-based chatbots for patients with depression and anxiety symptoms; AI-facilitated approaches, including virtual reality applications in anxiety disorders; avatar therapy for patients with psychosis; AI humanoid robot-enhanced therapy, for both children and the isolated elderly in care settings; AI animal-like robots to help patients with dementia; and digital game interventions for young people with mental health conditions. Overall, the studies showed positive effects and none reported any adverse side effects. However, the quality of the data was low, mainly due to a lack of studies, a high risk of bias and the heterogeneity of the studies. Importantly also, longer-term effects were often not evident. This suggests that translating small-scale, short-term trials into effective large-scale, longer-term real-world applications may be a particular challenge. While the use of AI in mental healthcare appears to have potential, its use also raises important ethical and privacy concerns, potential risk of bias, and the risk of unintended consequences such as over-diagnosis or unnecessary treatment of normal emotional experiences. More robust, longer-term research with larger patient populations, and clear regulatory frameworks and ethical guidelines to ensure that patients’ rights, privacy and well-being are protected, are therefore needed.
DOI: https://doi.org/10.37349/edht.2025.101143
This article belongs to the special issue Digital Health Innovations in the Battle Against Psychological Problems: Progress, Hurdles, and Prospects
