Keywords
Healthcare
Iran
Ethical Issues
Qualitative Study
Abstract
Background: Artificial intelligence (AI) is rapidly transforming healthcare by improving diagnostic accuracy, treatment planning, and operational efficiency. While AI adoption is advancing in high-income countries, low- and middle-income countries (LMICs), including Iran, face significant challenges that limit its integration. Iran’s health system is increasingly strained by an aging population, rising non-communicable diseases, and resource limitations. AI offers potential solutions to these issues by enabling data-driven, patient-centered care, yet its implementation remains in early stages within the country. Understanding the barriers to AI adoption in Iran’s health system is essential for guiding effective integration.
Methods: This qualitative study employed semi-structured interviews with 15 purposively selected stakeholders involved in healthcare management, policy-making, and AI application in Iran. Participants represented diverse sectors such as government, academia, healthcare provision, and technology development. Interviews were conducted between January 2025 and April 2025, both virtually and face-to-face. Data were analyzed thematically using Braun and Clarke’s framework to identify key challenges. Methodological rigor was ensured through member checking, triangulation, and adherence to qualitative research standards.
Results: Five major themes emerged as barriers to AI integration in Iran’s healthcare system: organizational and structural barriers, legal and policy constraints, data-related issues, skill and human resource challenges, and difficulties embedding AI within existing policymaking processes. Participants reported inadequate infrastructure, fragmented governance, and resistance to technological change. Legal ambiguities and lack of regulatory frameworks fostered ethical and accountability concerns. Data quality, security, and standardization were significant obstacles. The shortage of skilled professionals and insufficient training limited AI utilization. Additionally, integrating AI into traditional policymaking raised concerns about preserving human oversight and ethical decision-making.
Conclusion: The study reveals multifaceted challenges to AI adoption in Iran’s healthcare system, emphasizing the need for a coordinated strategy encompassing legal reforms, infrastructure investment, capacity building, and cultural change. Balancing AI’s technological benefits with ethical and human-centered care is critical for sustainable integration. These findings provide valuable insights to inform policy and practice, supporting responsible AI implementation in Iran’s healthcare system.
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