سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / اولین همایش بین المللی تحول خدمات سلامت: تلفیق مراقبت، درمان، بازتوانی و امیدبخشی

Applications and Implications of Artificial Intelligence in Health Data Governance: A Scoping Review of Ethical, Legal and Operational Challenges

نویسندگان :

Mina Anvari¹ Shima Anvari² Reihane Sharif³ Reza Abbasi⁴ Nafiseh Jamal⁵

1- دانشگاه علوم پزشکی ایران 2- دانشگاه علوم پزشکی کاشان 3- دانشگاه علوم پزشکی کاشان 4- دانشگاه علوم پزشکی تربت حیدریه 5- دانشگاه علوم پزشکی کاشان

کلمات کلیدی :

Artificial Intelligence،Information Management،Confidentiality،Health Policy،Ethics

چکیده :

Background: The spread of artificial intelligence (AI) across health systems is reshaping health data governance: AI consumes health data, generates derived data, and increasingly serves as a governance instrument itself. Yet the evidence is scattered across disciplines and lacks an integrated map. This study therefore aimed to map the applications and implications of AI in health data governance and the associated ethical, legal, and operational challenges. Method: This scoping review followed the JBI framework and PRISMA-ScR reporting guidance. PubMed/MEDLINE and Scopus were searched through 27 September 2026, yielding 1,005 records, of which 211 documents were included. Documents were coded by the role of AI (governance tool or governance object), data governance dimensions, challenges, and proposed solutions. Finding: Seventy-two percent of documents were published between 2024 and 2026. AI functioned as a governance tool, chiefly for de-identification, in 49% of documents, but as an object of governance in only 9%. Privacy was examined in 154 documents, whereas ownership and control (10) and secondary use (12) received little attention. Legal, ethical, and organizational challenges were the most frequent, yet proposed solutions were predominantly technical; auditing, stakeholder participation, and impact assessment were least represented. Field-based evidence constituted only 13% of the corpus, and no document originated from Iran in the searched databases. Conclusion: Institutional challenges are met largely with technical responses. Health data governance in the AI era cannot be reduced to de-identification; questions of licensing, accountability, and patients' share of data benefits demand institutional answers.