Human-AI Collaboration in Clinical Reasoning: A Framework for Safe Diagnostic Decision-Making

Artificial intelligence is rapidly transitioning from an experimental algorithm to a component of clinical workflow. In diagnostics, AI-based clinical decision support systems (AI-CDSS) and large language models (LLMs) are already capable of assisting physicians in analyzing data, formulating differential diagnoses, interpreting images, and improving documentation. However, the benefit of such systems depends not only on technical accuracy but also on the quality of human-AI interaction. This narrative review examines the conflict between evidence of benefit from human-AI collaboration and risks of hallucinations, temporal obsolescence, privacy violations, and erosion of clinical thinking. Based on analysis of contemporary reviews, a framework for balanced human-AI collaboration is proposed: AI should augment, not replace, clinical judgment; its role should be defined by task risk level; AI recommendations should undergo verification protocol; and physicians should be trained in trust calibration.