Exploration of Digital Health Technologies

Table 3. Highlights/take-home message.

Take home messages
AI highlights real-time surveillance and automation of food safety tasks
High-quality data and transparent algorithms are essential for trustworthy outcomes
Scalable infrastructure and strong digital data management are key to adaptation
Current challenges include privacy risks and system integration barriers
Public-private collaborations accelerate responsible adoption
AI complements, rather than replaces, human expertise in food safety decision-making

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Declarations

Author contributions

SM and ZEK: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. BB, LR, AC, ACR, MMDC: Investigation, Writing—review & editing. AA: Investigation, Writing—original draft, Writing—review & editing. VB and AN: Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

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References

Hoelzer K, Moreno Switt AI, Wiedmann M, Boor KJ. Emerging needs and opportunities in foodborne disease detection and prevention: From tools to people. Food Microbiol. 2018;75:65–71. [DOI] [PubMed]

European Food Safety Authority; European Centre for Disease Prevention and Control. The European Union One Health 2021 Zoonoses Report. EFSA J. 2022;20:e07666. [DOI] [PubMed] [PMC]

Tauxe RV, Doyle MP, Kuchenmüller T, Schlundt J, Stein CE. Evolving public health approaches to the global challenge of foodborne infections. Int J Food Microbiol. 2010;139:S16–28. [DOI] [PubMed]

Herrero M, Hugas M, Lele U, Wirakartakusumah A, Torero M. A Shift to Healthy and Sustainable Consumption Patterns. In: von Braun J, Afsana K, Fresco LO, Hassan MHA, editors. Science and Innovations for Food Systems Transformation. Cham: Springer; 2023. pp. 59–85. [DOI]

Singh B, Mishra A, Zoellner C, editors. Symposium 30: Applications of Artificial Intelligence and Machine Learning in Food Safety: An Update and Future Trends. International Association for Food Protection Annual Meeting; 2023 Jul 16–19; Toronto, Ontario, Canada. Advancing Food Safety Worldwide; 2023.

van der Fels‐Klerx HJ, van Asselt ED, Raley M, Poulsen M, Korsgaard H, Bredsdorff L, et al. Critical review of methodology and application of risk ranking for prioritisation of food and feed related issues, on the basis of the size of anticipated health impact. EFSA Support Publ. 2015;12:EN-710. [DOI]

Geng Z, Zhao S, Tao G, Han Y. Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety. Food Control. 2017;78:33–42. [DOI]

van Asselt ED, Noordam MY, Pikkemaat MG, Dorgelo FO. Risk-based monitoring of chemical substances in food: Prioritization by decision trees. Food Control. 2018;93:112–20. [DOI]

Hartman J. Predictive Models for Food Code Violations. Food Prot Trends. 2020;40:56–65.

10.

Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark. 2021;31:685–95. [DOI]

11.

Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput Oper Res. 2020;119:104926. [DOI]

12.

Kakani V, Van Huan N, Basivi PK, Kim H, Pasupuleti VR. A critical review on computer vision and artificial intelligence in food industry. J Agric Food Res. 2020;2:100033. [DOI]

13.

Pandey VK, Srivastava S, Dash KK, Singh RK, Shaikh AM, Kovács B, et al. Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review. Processes. 2023;11:1720. [DOI]

14.

Enabling rapid outbreak analytics with large language models [Internet]. Billy Quilty; c2025 [cited 2024 Jan 15]. Available: https://billyquilty.substack.com/p/llmlinelist-enabling-rapid-outbreak

15.

Wang H, Cui W, Guo Y, Du Y, Zhou Y. Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study. JMIR Med Inform. 2021;9:e24924. [DOI] [PubMed] [PMC]

16.

Qian C, Murphy SI, Orsi RH, Wiedmann M. How Can AI Help Improve Food Safety? Annu Rev Food Sci Technol. 2023;14:517–38. [DOI] [PubMed]

17.

Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. Building machines that learn and think like people. Behav Brain Sci. 2017;40:e253. [DOI] [PubMed]

18.

Du Y, Guo Y. Machine learning techniques and research framework in foodborne disease surveillance system. Food Control. 2022;131:108448. [DOI]

19.

Zhang P, Cui W, Wang H, Du Y, Zhou Y. High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors. Foodborne Pathog Dis. 2021;18:590–8. [DOI] [PubMed] [PMC]

20.

Ma L, Yi J, Wisuthiphaet N, Earles M, Nitin N. Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging. Appl Environ Microbiol. 2023;89:e0182822. [DOI] [PubMed] [PMC]

21.

Magrani E. New perspectives on ethics and the laws of artificial intelligence. Internet Policy Rev. 2019;8. [DOI]

22.

Lin X, Li J, Han Y, Geng Z, Cui S, Chu C. Dynamic risk assessment of food safety based on an improved hidden Markov model integrating cuckoo search algorithm: a sterilized milk study. J Food Process Eng. 2021;44:e13630. [DOI]

23.

Onyeaka H, Akinsemolu A, Miri T, Nnaji ND, Emeka C, Tamasiga P, et al. Advancing food security: The role of machine learning in pathogen detection. Appl Food Res. 2024;4:100532. [DOI]

24.

Rasheed HH, Salih AA, Ahmed OM, Yazdeen AA, Abdullah RM, Sami TMG. Consideration of cloud-web-concepts for standardization and interoperability: a comprehensive review for sustainable enterprise systems, AI, and IoT integration. J Inf Technol Inf. 2024;3:20–38.

25.

Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA. Application of Artificial Intelligence in Food Industry-a Guideline. Food Eng Rev. 2022;14:134–75. [DOI] [PubMed] [PMC]

26.

An Overview of Emerging Global AI Regulations [Internet]. Securiti; c2025 [cited 2025 Sep 17]. Available: https://securiti.ai/ai-regulations-around-the-world/

27.

Mohammed Aarif KO, Alam A, Hotak Y. Smart Sensor Technologies Shaping the Future of Precision Agriculture: Recent Advances and Future Outlooks. J Sens. 2025;2025:2460098. [DOI]

28.

Sasikala B, Sachan S. Decoding decision-making: embracing explainable AI for trust and transparency. In: Mirza AU, Kumar B, editors. Exploring the Frontiers of Artificial Intelligence and Machine Learning Technologies. Chennai: San International Scientific Publication; 2024. pp. 42–62. [DOI]

29.

Kuppusamy S, Meivelu M, Praburaman L, Alam MM, Al-Sehemi AG, Anbarasu K. Integrating AI in food contaminant analysis: Enhancing quality and environmental protection. J Hazard Mater Adv. 2024;16:100509. [DOI]

30.

Dhal SB, Kar D. Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review. Discov Appl Sci. 2025;7:75. [DOI]

31.

Ferrera E. Fairness and bias in artificial intelligence: a brief survey of sources, impacts, and mitigation strategies. Sci. 2024;6:3. [DOI]