Tabriz University of Medical Sciences Depiction of Health 2008-9058 17 2 2026 06 20 Interdisciplinary AI Research Linking Medical and Engineering Sciences: An Analysis of Barriers, Governance Mechanisms, and Policy Recommendations 188 208 10.34172/doh.2026.15 EN Yaser Davodi https://orcid.org/0009-0005-5350-942X Armita Pak https://orcid.org/0009-0009-0335-7631 Ali Najafi https://orcid.org/0009-0002-6972-2389 Moein Rahmani https://orcid.org/0009-0003-8103-9512 Omid Gheisavandi https://orcid.org/0009-0008-7071-2085 Journal Article 10.34172/doh.2026.15 2025 12 27 2026 06 22 Background. Rapid advancements in Artificial Intelligence (AI) hold transformative potential for health systems, yet the complexity of medical problems requires a deeply integrated, interdisciplinary approach. Currently, structural disconnects and the absence of common language between the engineering and medical fields hinder the translation of technical concepts into effective clinical solutions. This policy brief identifies structural challenges and provides operational recommendations to strengthen joint research and bridge the translation gap. Methods. This study employed a mixed-methods design in two phases. Phase one was a structured brainstorming workshop held on November 6, 2025, with 20 participants: 15 talented students (10 from medical sciences, 5 from engineering) and 5 faculty members from medical sciences and computer engineering at Tehran University of Medical Sciences and Sharif University of Technology. Workshop discussions were recorded, transcribed, and analyzed using qualitative content analysis to identify key themes. Phase two consisted of a targeted review of international best practices and relevant literature to complement the findings and formulate policy options. Results. The analysis identified six primary domains of challenges: (1) Inadequate infrastructure for interdisciplinary research; (2) Deficiencies in the educational environment and student empowerment; (3) Difficulty establishing a common conceptual framework for AI-related problems; (4) Knowledge translation gap; (5) Procedural barriers to collaboration; and (6) Weaknesses in policy and institutional governance. Key policy recommendations include establishing joint research centers and utilizing Edge AI, implementing skills-based training for physicians, creating an overarching governance body to standardize education and ethics, fostering industry-academia partnerships through reformed administrative mechanisms, and developing a transparent legal framework to support innovation. Conclusion. The primary barrier in AI health research is not a deficit in technical knowledge but the absence of an integrated ecosystem. For the medical domain, active involvement in model design and validation is essential, while for engineering, targeted access to real-world clinical data and understanding clinical requirements are crucial. Policymakers are advised to facilitate the transition from isolated research to convergent innovation by adopting a phased approach to educational integration, actively supporting inter-professional education, and establishing secure legal and ethical foundations to foster trust and collaboration. Research Insight · The advancement of Artificial Intelligence in healthcare is primarily dependent on the authentic convergence of medicine and engineering and the establishment of a shared conceptual framework, rather than purely technological progression. · Fragmented data infrastructure, a lack of sustained collaboration, and deficient research governance are the primary drivers of the gap between interdisciplinary knowledge production and clinical implementation. · Current educational models lack problem-oriented approaches and structured interdisciplinary pathways, leading to the suboptimal mobilization of human capital in addressing complex healthcare challenges. · Weak linkages between academia, industry, and the clinical sector create a translational bottleneck, leaving a significant portion of AI innovations stalled in the development or validation phases without reaching clinical deployment. · This policy brief shows that problem solving requires a systemic reconfiguration of the nexus between knowledge generation, professional education, and health service delivery, shifting the focus from a singular reliance on technology to structural reform.