Enhancing clinical reasoning and diagnostic precision through scaling laws and multi-stage supervised fine-tuning in open-weight medical large language models

Transforming large language models into medical specialists via knowledge injection.

While general-purpose large language models (LLMs) demonstrate remarkable capabilities, their clinical application demands rigorous adaptation to ensure safety and accuracy. This review presents a comprehensive framework for transforming LLMs into trustworthy medical specialists. We detail three core knowledge-injection strategies-(1) static embedding to internalize foundational biomedical knowledge; (2) behavioral alignment to enforce clinical safety and verifiable diagnostic logic; and (3) dynamic injection, such as retrieval-augmented generation, for real-time evidence grounding-together with multimodal integration as a complementary perception-injection paradigm extending the input space beyond text to imaging, biosignals, and tabular data. Building on these strategies, we further explore the evolution toward agentic AI systems that orchestrate them for autonomous, collaborative clinical decision-making. Finally, we discuss critical challenges, including model calibration, resource constraints, standardized reporting, and robust safety protocols. Combining these complementary strategies is essential for developing deployable, domain-specialized clinical AI systems.

Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction

CAST (Concept-guided Artifact Suppression Tuning), an SAE-based framework for auditable clinical text classification, improves over its corresponding fine-tuned encoder baselines and remains competitive with strong LLM baselines, while producing a feature-level audit trail of the clinical concepts that support each prediction and the artifact concepts suppressed during training.

Decoding high-order clinical correlations: a knowledge-driven large language model framework for specialized medical decision-making

Embedding domain-specific knowledge into LLMs may improve performance on specialized exam-style thoracic-surgery questions on this text-only benchmark, however, the present 56-item evaluation does not establish clinical equivalence, diagnostic accuracy in practice, multimodal competence, or readiness for real-world clinical decision support.

Tutorial: guidance on the use of large language models for medical research

This entry-level tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.

The Application of Large Language Models in Medical Question‑Answering Robots

Model and Task-Aware Test-Time Scaling Strategies for Large Language and Vision-Language Models in Medicine: Evaluation Study

Abstract Background Test-time scaling has emerged as a promising method to enhance the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) during inference without additional training. While foundational studies established scaling paradigms in general domains, their applicability to the unique complexities of medical AI remains underexplored. Objective This study aims to conduct a comprehensive investigation of test-time scaling in the medical domain. We evaluate the impact of scaling across different model sizes and task complexities. Furthermore, we seek to identify domain-specific bottlenecks and assess model robustness against user-driven perturbations, such as misleading clinical authority. Methods This study evaluated a diverse set of general and medical-specific LLMs and VLMs. Experiments used five textual medical benchmarks comprising over 5500 questions and two multimodal benchmarks comprising 7000 samples. Performance was measured under three scaling conditions: increasing token budgets, iterative sequential scaling, and parallel scaling. Robustness was tested by embedding misleading hints with varying tones and levels of simulated clinical expertise into prompts. Results For nonreasoning LLMs, accuracy saturated quickly, with token usage often remaining under 500 tokens regardless of budget increases. Reasoning models demonstrated significant performance gains on complex tasks as token budgets increased. Notably, we identified distinct domain-specific behaviors. First, current VLMs showed a structural bottleneck in integrating visual clues and experienced limited benefit from token expansion. Second, medically fine-tuned LLMs excelled in clinical question answering but exhibited degraded scaling efficiency on calculation tasks compared to general-domain models. This reflects a disparity between qualitative clinical alignment and procedural logic. Third, while optimal scaling improved robustness, models exhibited a cognitive vulnerability by readily abandoning correct reasoning when confronted with misleading expert physician hints. Regarding scaling strategies, parallel scaling outperformed sequential scaling on easier tasks. Conversely, extended sequential scaling or increased budgets proved essential for complex problem-solving. Conclusions Test-time scaling rules from general domains do not perfectly translate to medical AI. Longer reasoning is not universally beneficial. Concise reasoning with parallel scaling is optimal for simpler tasks. An extended chain of thought via sequential scaling or increased budgets is required for complex problems. Furthermore, safe clinical deployment requires addressing fundamental vision-language alignment, balancing clinical and procedural reasoning, and mitigating vulnerabilities to perceived clinical authority.