Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

Overview of Medical LLM Reasoning

This survey provides a structured framework for evaluating Large Language Models (LLMs) in clinical settings. The authors propose a dual-view approach that maps computational reasoning patterns—deductive, inductive, and abductive—to clinical competency levels derived from Miller’s Pyramid. This methodology aims to bridge the gap between abstract AI capabilities and the practical, high-stakes requirements of medical workflows.

Clinical and Computational Frameworks

The authors categorize medical reasoning into a five-level competency scheme, ranging from basic knowledge recognition to complex, dynamic case management. By aligning these levels with specific reasoning paradigms, the paper creates a roadmap for understanding how different AI architectures (e.g., retrieval-augmented, agentic, or multimodal models) address clinical tasks like symptom normalization, differential diagnosis, and treatment planning. This taxonomy helps clarify why certain models succeed in specific clinical domains while failing in others.

Performance Trends and Benchmarking

To standardize evaluation, the authors introduced a new benchmark dataset containing 5,000 samples across five levels of reasoning. Testing 18 state-of-the-art models revealed that performance is not merely a function of model size. Instead, the quality of instruction tuning, the inclusion of domain-specific data, and the implementation of reasoning-focused training (such as chain-of-thought or agentic workflows) are critical drivers of success. The findings suggest a hybrid deployment strategy: routing diagnosis-heavy queries to specialized medical models while utilizing general-purpose models for patient dialogue and administrative support.

Future Directions

The paper highlights persistent challenges, including model hallucination, data grounding, and the difficulty of predicting structured temporal outcomes. The authors argue that future development must move beyond simple accuracy metrics toward evaluating reasoning completeness, evidence-based factuality, and clinical safety to ensure these systems are truly ready for real-world deployment.