Currently submitted to: Journal of Medical Internet Research
Date Submitted: Mar 26, 2026
Open Peer Review Period: Mar 27, 2026 - May 22, 2026
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Clinician-Led Artificial Intelligence Innovation: A Practical Guide to Building Custom Healthcare Applications Without Traditional Programming
ABSTRACT
The development of artificial intelligence applications in healthcare has historically required multidisciplinary technical teams, substantial programming expertise, and prolonged development cycles, effectively excluding clinicians, the professionals with the deepest contextual understanding of clinical problems, from the role of active builder. The emergence of no-code development platforms, vibe coding tools, and AI workflow orchestration systems has fundamentally altered this landscape, creating a realistic pathway for clinicians, medical educators, and healthcare researchers to design, build, and deploy custom AI-powered applications without traditional programming knowledge. This tutorial presents a structured five-level clinician-builder development framework spanning conversational chatbot construction using no-code, vibe coding and AI coding assistants. The Clinician-Led Evidence-grounded AI Development framework is proposed as a reproducible methodology for guiding clinician-initiated AI development projects from clinical problem identification through deployment and monitoring. This tutorial demonstrates that the technical barriers separating clinical insight from AI application development have been substantially lowered, and that the tools required for clinician-led innovation in healthcare AI are now accessible, practical, and ready for responsible deployment
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