Reverse Physician-AI Relationship: Full-process Clinical Diagnosis Driven by a Large Language Model

2025-08-14

AI Summary

Current clinical AI systems only respond to specific diagnostic queries posed by physicians, lacking the capability to autonomously initiate and drive end-to-end diagnostic workflows from vague patient chief complaints—severely limiting their clinical utility. To address this, we propose a “Physician–AI Role Reversal” paradigm, centered on our custom large language model, DxDirector-7B, which integrates full-process reasoning, multi-task diagnostic modeling, and traceable responsibility attribution. This enables AI-led, physician-aided end-to-end diagnosis. Evaluated on rare diseases, complex cases, and real-world clinical scenarios, DxDirector-7B significantly outperforms leading medical and general-purpose LMs in both diagnostic accuracy and efficiency. Expert blind evaluations indicate its potential to replace subspecialist physicians for initial diagnostic triage. To our knowledge, this is the first AI-driven diagnostic framework with explicitly defined roles and accountability, establishing a novel pathway for paradigmatic advancement in clinical AI.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Algorithmic accountability and transparency on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Full-process clinical diagnosis in the real world encompasses the entire diagnostic workflow that begins with only an ambiguous chief complaint. While artificial intelligence (AI), particularly large language models (LLMs), is transforming clinical diagnosis, its role remains largely as an assistant to physicians. This AI-assisted working pattern makes AI can only answer specific medical questions at certain parts within the diagnostic process, but lack the ability to drive the entire diagnostic process starting from an ambiguous complaint, which still relies heavily on human physicians. This gap limits AI's ability to fully reduce physicians' workload and enhance diagnostic efficiency. To address this, we propose a paradigm shift that reverses the relationship between physicians and AI: repositioning AI as the primary director, with physicians serving as its assistants. So we present DxDirector-7B, an LLM endowed with advanced deep thinking capabilities, enabling it to drive the full-process diagnosis with minimal physician involvement. Furthermore, DxDirector-7B establishes a robust accountability framework for misdiagnoses, delineating responsibility between AI and human physicians. In evaluations across rare, complex, and real-world cases under full-process diagnosis setting, DxDirector-7B not only achieves significant superior diagnostic accuracy but also substantially reduces physician workload than state-of-the-art medical LLMs as well as general-purpose LLMs. Fine-grained analyses across multiple clinical departments and tasks validate its efficacy, with expert evaluations indicating its potential to serve as a viable substitute for medical specialists. These findings mark a new era where AI, traditionally a physicians' assistant, now drives the entire diagnostic process to drastically reduce physicians' workload, indicating an efficient and accurate diagnostic solution.

Problem

• AI lacks ability to drive full clinical diagnosis from ambiguous complaints
• Current AI-assisted models limit workload reduction and diagnostic efficiency
• Need for AI to lead diagnosis with minimal physician involvement

Innovation

• LLM drives full-process clinical diagnosis
• AI as primary director, physicians assist
• Accountability framework for misdiagnoses established