Symbiotic AI Diagnosis: A Reverse Mentorship Framework for Clinical Decision Support through Real-Time Physician Training by Machine Learning Models

This study proposes a novel conceptual framework, Symbiotic AI Diagnosis, grounded in the principle of reverse mentorship within AI-driven clinical decision support systems (CDSS). While traditional approaches position artificial intelligence as an assistive tool for clinicians, this work argues that AI can also function as a real-time educational agent for physicians. Through explainable AI (XAI) mechanisms, machine learning models not only generate predictions but also provide interpretable explanations, thereby transforming CDSS into active learning systems. Based on a targeted synthesis of the literature, the proposed framework defines a bidirectional learning loop between AI systems and clinicians, consisting of three iterative phases: explanation delivery, clinician reflection and action, and feedbackdriven model adaptation. The framework introduces novel evaluation dimensions such as diagnostic calibration, knowledge transfer efficacy, and heuristic evolution to assess the educational impact of AI in clinical settings. It is argued that reframing AI as a “teaching partner” rather than a passive decision-support tool can enhance continuous professional development, improve clinical reasoning, and strengthen human–AI collaboration. In conclusion, the Symbiotic AI Diagnosis model offers a shift from augmentation-oriented AI toward a learning-oriented paradigm, with the potential to foster more transparent, adaptive, and human-centered healthcare systems.