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.