Irtiqa Imini Pishgiri Masdumiyat (Safety Promotion and Injury Prevention)

Vol. 14 No. 1 (1405)

Health Data Governance in the Age of Artificial Intelligence: Challenges, Requirements, and Strategies

Irtiqa Imini Pishgiri Masdumiyat (Safety Promotion and Injury Prevention), Vol. 14 No. 1 (1405), 16 October 2026 , Page 31-43
https://doi.org/10.22037/iipm.v14i1.53170

Abstract

The digital transformation of the health system and the expansion of AI applications have elevated the status of health data from an operational resource to a strategic asset for clinical decision-making, public policy-making, and technological innovation. However, the safe, fair, and effective use of data depends on the establishment of a coherent framework for health data governance. Otherwise, challenges such as privacy violations, weakening public trust, legal gaps, lack of algorithmic transparency, and reproduction of existing inequalities in the form of data biases can jeopardize the legitimacy and effectiveness of AI in health. The present study, using a review-analytic approach, explores the conceptual, institutional, and policy dimensions of health data governance in the era of AI. To this end, while systematically reviewing the relevant literature, the evolution of data governance patterns from institutional control to individual control and then collective governance is explained, and the interrelationship between the quality of data governance and the deployability of AI is analyzed. In addition, using the global ODIN index, Iran’s situation has been assessed in terms of data coverage and openness, two important components of data readiness for AI. The findings indicate that the main problem in Iran is not related to a lack of technical capacity, but rather to structural shortcomings in data governance, including information silos, weak metadata, lack of a national data strategy, and lack of transparent frameworks for secondary use of data. Based on these findings, the paper presents a three-layer framework for modern health data governance in Iran that includes the layers of infrastructure and technology, protection and legislation, and ethics and collective participation. The final conclusion is that the responsible deployment of AI in the Iranian health system requires a transition from traditional and control-oriented governance to participatory, standards-oriented, and justice-oriented governance.

Keywords:
  • Algorithmic bias
  • Artificial intelligence
  • Health data governance
  • Interoperability
  • ODIN
  • Privacy
  • data protection

How to Cite

1.
طاهری ق, Emamgholipour S, Safari M, Kamyab Z. Health Data Governance in the Age of Artificial Intelligence: Challenges, Requirements, and Strategies. Irtiqa Imini Pishgiri Masdumiyat [Internet]. 2026 Oct. 16 [cited 2026 Nov. 1];14(1):31-43. Available from: https://journals.sbmu.ac.ir/spip/article/view/53170

References

1. Gudepu BK, Eichler R. The role of AI in enhancing data governance strategies. International Journal of Acta Informatica. 2024;3(1):169-186. https://www.yuktabpublisher.com/index.php/IJAI/article/view/196

2. Holly L, Thom S, Elzemety M, Murage B, Mathieson K, Iñigo Petralanda MI. Strengthening health data governance: new equity and rights-based principles. International Journal of Health Governance. 2023;28(3):225-237. https://doi.org/10.1108/IJHG-11-2022-0104

3. de Freitas C, Amorim M, Machado H, Teles EL, Baptista MJ, Renedo A, Provoost V, Silva S. Public and patient involvement in health data governance (DATAGov): protocol of a people-centred, mixed-methods study on data use and sharing for rare diseases care and research. BMJ Open. 2021;11(3):e044289. https://doi.org/10.1136/bmjopen-2020-044289

4. Kickbusch I, Piselli D, Agrawal A, Balicer R, Banner O, Adelhardt M, Capobianco E, et al. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. The Lancet. 2021;398(10312):1727-1776. https://doi.org/10.1016/S0140-6736(21)01824-9

5. World Health Organization. Health data as a global public good – a call for Health Data Governance. Summit statement. 30 September 2021. https://www.who.int/news-room/articles-detail/health-data-as-a-global-public-good-a-call-for-health-data-governance-30-september

6. Vayena E, Dzenowagis J, Brownstein JS, Sheikh A. Policy implications of big data in the health sector. Bulletin of the World Health Organization. 2018;96(1):66-68. https://doi.org/10.2471/BLT.17.197426

7. Borges do Nascimento IJ, Marcolino MS, Abdulazeem HM, Weerasekara I, Azzopardi-Muscat N, Gonçalves MA, Novillo-Ortiz D. Impact of big data analytics on people’s health: overview of systematic reviews and recommendations for future studies. Journal of Medical Internet Research. 2021;23(4):e27275. https://doi.org/10.2196/27275

8. World Health Organization Regional Office for Europe. Seventy-first Regional Committee for Europe: virtual session, 13–15 September 2021: guide for participants. EUR/RC71/DIV/2. 2021.

9. Organisation for Economic Co-operation and Development. Recommendation of the Council on Health Data Governance. Paris: Organisation for Economic Co-operation and Development; 2016. OECD/LEGAL/0433. OECD official legal instrument

10. Organisation for Economic Co-operation and Development. Health Data Governance for the Digital Age: Implementing the OECD Recommendation on Health Data Governance. Paris: OECD Publishing; 2022. https://doi.org/10.1787/68b60796-en

11. World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. WHO official publication

12. Zhang J, Zhang ZM. Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making. 2023;23(1):7. https://doi.org/10.1186/s12911-023-02103-9

13. Hanna MG, Pantanowitz L, Jackson B, Palmer O, Visweswaran S, Pantanowitz J, Deebajah M, Rashidi HH. Ethical and bias considerations in artificial intelligence/machine learning. Modern Pathology. 2025;38(3):100686. https://doi.org/10.1016/j.modpat.2024.100686

14. Morley J, Murphy L, Mishra A, Joshi I, Karpathakis K. Governing data and artificial intelligence for health care: developing an international understanding. JMIR Formative Research. 2022;6(1):e31623. https://doi.org/10.2196/31623

15. World Health Organization Regional Office for Europe. Health data governance in the age of artificial intelligence: policy imperatives for the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2025. WHO/EURO:2025-11462-51234-78079.

16. Winter JS, Davidson E. Big data governance of personal health information and challenges to contextual integrity. The Information Society. 2019;35(1):36-51. https://doi.org/10.1080/01972243.2018.1542648

17. Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implementation Science. 2010;5:69. https://doi.org/10.1186/1748-5908-5-69

18. Arksey H, O’Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005;8(1):19-32. https://doi.org/10.1080/1364557032000119616

19. Were V, Moturi C. Toward a data governance model for the Kenya health professional regulatory authorities. The TQM Journal. 2017;29(4):579-589. https://doi.org/10.1108/TQM-10-2016-0092

20. Arigbabu AT, Olaniyi OO, Adigwe CS, Adebiyi OO, Ajayi SA. Data governance in AI-enabled healthcare systems: a case of the Project Nightingale. Asian Journal of Research in Computer Science. 2024;17(5):85-107. https://doi.org/10.9734/ajrcos/2024/v17i5441

21. Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, Floridi L. The ethics of AI in health care: a mapping review. Social Science & Medicine. 2020;260:113172. https://doi.org/10.1016/j.socscimed.2020.113172

22. Adigwe CS, Olaniyi OO, Olabanji SO, Okunleye OJ, Mayeke NR, Ajayi SA. Forecasting the future: the interplay of artificial intelligence, innovation, and competitiveness and its effect on the global economy. Asian Journal of Economics, Business and Accounting. 2024;24(4):126-146. https://doi.org/10.9734/ajeba/2024/v24i41269

23. Wirtz BW, Weyerer JC, Sturm BJ. The dark sides of artificial intelligence: an integrated AI governance framework for public administration. International Journal of Public Administration. 2020;43(9):818-829. https://doi.org/10.1080/01900692.2020.1749851

24. Olaniyi FG, Olaniyi OO, Adigwe CS, Abalaka AI, Shah N. Harnessing predictive analytics for strategic foresight: a comprehensive review of techniques and applications in transforming raw data to actionable insights. Asian Journal of Economics, Business and Accounting. 2023;23(22):441-459. https://doi.org/10.9734/ajeba/2023/v23i221164

25. Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making. 2020;20(1):310. https://doi.org/10.1186/s12911-020-01332-6

26. Olabanji SO, Marquis YA, Adigwe CS, Ajayi SA, Oladoyinbo TO, Olaniyi OO. AI-driven cloud security: examining the impact of user behavior analysis on threat detection. Asian Journal of Research in Computer Science. 2024;17(3):57-74. https://doi.org/10.9734/ajrcos/2024/v17i3424

27. Henderson B, Flood CM, Scassa T. Artificial intelligence in Canadian healthcare: will the law protect us from algorithmic bias resulting in discrimination? Canadian Journal of Law and Technology. 2022;19(2):475-504. https://digitalcommons.schulichlaw.dal.ca/cjlt/vol19/iss2/10/

28. Oladoyinbo TO, Olabanji SO, Olaniyi OO, Adebiyi OO, Okunleye OJ, Alao AI. Exploring the challenges of artificial intelligence in data integrity and its influence on social dynamics. Asian Journal of Advanced Research and Reports. 2024;18(2):1-23. https://doi.org/10.9734/ajarr/2024/v18i2601

29. Vlachos I. Implementation of an intelligent supply chain control tower: a socio-technical systems case study. Production Planning & Control. 2023;34(15):1415-1431. https://doi.org/10.1080/09537287.2021.2015805

30. Li C, Wang C, Chau PYK. Revealing the black box: understanding how prior self-disclosure affects privacy concern in the on-demand services. International Journal of Information Management. 2022;67:102547. https://doi.org/10.1016/j.ijinfomgt.2022.102547

31. Iqbal M, Zahidie A. Diffusion of innovations: a guiding framework for public health. Scandinavian Journal of Public Health. 2022;50(5):533-537. https://doi.org/10.1177/14034948211014104

32. Winter JS, Davidson E. Investigating values in personal health data governance models. In: AMCIS 2017 Proceedings. 2017;15.

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