Abstract
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.
Extended Abstract
Background
In the modern era, complex health challenges increasingly demand interdisciplinary solutions that transcend traditional disciplinary boundaries. Artificial Intelligence (AI) offers a transformative force in healthcare, with unprecedented capabilities for analyzing large-scale datasets and supporting evidence-based clinical decision-making. Despite AI's potential to enhance healthcare quality, safety, and accessibility, the formation of effective interdisciplinary teams comprising medical and engineering professionals faces significant structural, educational, and policy-related barriers. Many AI-in-medicine initiatives fail to bridge the critical translation gap,from algorithm to clinical implementation,due to fundamental communication barriers between engineers and physicians, compounded by institutional fragmentation between health and science ministries. This disconnects leads to technically sophisticated models that lack clinical relevance and clinically important problems that remain unsolved due to inadequate technical engagement. This policy brief systematically identifies these structural challenges and proposes evidence-based interventions to foster meaningful collaboration, ensuring that technological innovations address authentic clinical needs and improve patient outcomes.
Methods This policy brief employs a mixed-methods strategic analysis combining stakeholder engagement with evidence synthesis. Primary data were gathered through a workshop conducted on November 6, 2025, convening key stakeholders including faculty and graduate students from Tehran University of Medical Sciences (TUMS) and Sharif University of Technology. The session was designed to facilitate open dialogue around two central questions: (1) What structural challenges impede the formation of successful AI-medicine research teams? (2) How can the conceptual and linguistic divide between these disciplines be effectively bridged? Qualitative data from the session underwent thematic analysis and were triangulated with findings from a systematic review of international best practices and relevant literature. Identified challenges were systematically categorized into six thematic domains, and corresponding policy options were evaluated using criteria including feasibility, potential impact, resource requirements, and implementation complexity to develop an actionable policy roadmap.
Results
The analysis identified critical challenges across six interconnected domains, each accompanied by specific, with corresponding policy recommendations:
1. Infrastructure deficits Hardware limitations and international sanctions constrain computational capacity. Recommendations: establishing international collaborative partnerships for knowledge and technology transfer; implementing Edge AI and model compression techniques to enable algorithm deployment on existing medical devices; and creating joint interdisciplinary research centers to reduce physical and institutional distance between engineering and medical faculties.
2. Educational gaps Current medical and engineering curricula lack adequate cross-disciplinary training. Recommendations: develop short-term continuing education programs in AI literacy for clinicians; establishing an overarching governance body to standardize competencies across institutions, and implementing longitudinal curriculum integration embedding AI principles throughout medical training rather than as isolated modules.
3. Communication barriers Disciplinary silos hinder mutual understanding. Recommendations: creating convergent work environments through reciprocal internships allowing engineers to observe clinical workflows and physicians to engage in algorithm development; developing shared conceptual frameworks including unified glossaries and ontologies; and organizing annual interdisciplinary competitions with diverse expert and public judging panels to foster mutual appreciation.
4. The Translation gap Research often remains disconnected from clinical implementation. Recommendations: reform administrative processes to facilitate engineer integration into hospital settings; establishing a comprehensive and ethically governed health data infrastructure; and ensuring physician co-design from project inception to validate models against clinically meaningful endpoints and workflows.
5. Collaboration impediments Lack of institutional support undermines team formation and sustainability. Recommendation: institutionalizing Inter-Professional Education (IPE) in both medical and engineering training; Aligning incentive structures to reward interdisciplinary publications and innovations; and developing legal and ethical frameworks clarifying liability, data governance, and intellectual property rights in collaborative projects.
6. Policy and organizational fragmentation Separation between health and science ministries creates systemic barriers. Recommendations: implementing phased educational integration between health and science ministries to harmonize academic calendars, funding mechanisms, and accreditation standards; and convening regular multi-stakeholder policy forums ensuring sustained engagement between government, academia, and healthcare delivery organizations.
Conclusion
The integration of AI into healthcare systems represents both an imperative and an opportunity for transforming patient care. However, this analysis reveals that the principal obstacles are not a a lack of expertise but rather structural fragmentation and the absence of a collaborative ecosystem. The institutional separation of educational, research, and clinical care has created isolated domains of excellence that fail to synergize effectively. Successful implementation of the recommended policies requires a fundamental paradigm shift from siloed research toward convergent innovation. By establishing robust legal and ethical frameworks, fostering integrated professional education, and creating shared physical and intellectual infrastructure, policymakers can substantially reduce coordination costs and unlock the full potential of interdisciplinary research. The proposed roadmap emphasizes phased implementation, beginning with pilot programs at engaged institutions, systematic evaluation of outcomes, and iterative refinement based on evidence. Success requires sustained commitment from government agencies, academic institutions, and healthcare organizations, along with mechanisms for ongoing stakeholder engagement and adaptive policy-making. Ultimately, bridging the divide between engineering innovation and medical practice will ensure that AI evolves from a promising technology into a reliable, safe, and effective instrument for improving population health and advancing precision medicine. The transformation of healthcare through AI is not merely a technical challenge but a systems-level imperative requiring coordinated action across multiple sectors and sustained investment in human capital, institutional capacity, and collaborative infrastructure.
Practical Implications of Research
The key practical implications include strengthening health data infrastructure and governance to enable AI research while protecting patient privacy. Equipping clinicians with AI literacy through redesigned, competency-based curricula will foster effective collaboration with engineers. Establishing interdisciplinary centres and shared pilot projects with clinically meaningful benchmarks can bridge the translation gap. Finally, reforming administrative procedures and developing clear ethical-legal frameworks will facilitate trusted industry-hospital partnerships and responsible deployment of AI in healthcare.