# Reasoning LLMs in the Medical Domain: A Literature Survey

arXiv: https://arxiv.org/abs/2508.19097 · HTML: https://mlsanity.com/paper/2508.19097/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 25 of 25)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution](https://mlsanity.com/paper/2502.18449/) (2025)
- [Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning](https://mlsanity.com/paper/2502.14768/) (2025)
- [Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models](https://mlsanity.com/paper/2503.13939/) (2025)
- [Holistic Evaluation of Language Models](https://mlsanity.com/paper/2211.09110/) (2022)
- [CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis](https://mlsanity.com/paper/2407.13301/) (2024)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records](https://mlsanity.com/paper/2503.04176/) (2025)
- [KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis](https://mlsanity.com/paper/2412.16833/) (2024)
- [MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation](https://mlsanity.com/paper/2503.13856/) (2025)
- [MC-CoT: A Modular Collaborative CoT Framework for Zero-shot Medical-VQA with LLM and MLLM Integration](https://mlsanity.com/paper/2410.04521/) (2024)
- [MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration](https://mlsanity.com/paper/2410.12532/) (2024)
- [Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies](https://mlsanity.com/paper/2501.17030/) (2025)
- [Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models](https://mlsanity.com/paper/2501.18645/) (2025)
- [Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment](https://mlsanity.com/paper/2503.12847/) (2025)
- [Chain-of-Though (CoT) prompting strategies for medical error detection and correction](https://mlsanity.com/paper/2406.09103/) (2024)
- [AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients](https://mlsanity.com/paper/2502.15944/) (2025)
- [A PubMedBERT-based Classifier with Data Augmentation Strategy for Detecting Medication Mentions in Tweets](https://mlsanity.com/paper/2112.02998/) (2021)
- [On the Performance of an Explainable Language Model on PubMedQA](https://mlsanity.com/paper/2504.05074/) (2025)
- [Eir: Thai Medical Large Language Models](https://mlsanity.com/paper/2409.08523/) (2024)

## Built on by (most cited first, 6 of 6)

- [EDCO: Dynamic Curriculum Orchestration for Domain-specific Large Language Model Fine-tuning](https://mlsanity.com/paper/2601.03725/) (2026)
- [Who Judges the Judge? Evaluating LLM-as-a-Judge for French Medical open-ended QA](https://mlsanity.com/paper/2603.04033/) (2026)
- [On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery](https://mlsanity.com/paper/2510.12640/) (2025)
- [Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches](https://mlsanity.com/paper/2606.01145/) (2026)
- [Evaluating LLM-Generated Rules for Heart Disease Prediction](https://mlsanity.com/paper/2609.13192/) (2026)
- [Taking the PULSE of Medical Reasoning: Step-Level Retrieval Timing for Knowledge-Grounded Inference](https://mlsanity.com/paper/2610.20292/) (2026)

## Abstract (arXiv)

The emergence of advanced reasoning capabilities in Large Language Models (LLMs) marks a transformative development in healthcare applications. Beyond merely expanding functional capabilities, these reasoning mechanisms enhance decision transparency and explainability-critical requirements in medical contexts. This survey examines the transformation of medical LLMs from basic information retrieval tools to sophisticated clinical reasoning systems capable of supporting complex healthcare decisions. We provide a thorough analysis of the enabling technological foundations, with a particular focus on specialized prompting techniques like Chain-of-Thought and recent breakthroughs in Reinforcement Learning exemplified by DeepSeek-R1. Our investigation evaluates purpose-built medical frameworks while also examining emerging paradigms such as multi-agent collaborative systems and innovative prompting architectures. The survey critically assesses current evaluation methodologies for medical validation and addresses persistent challenges in field interpretation limitations, bias mitigation strategies, patient safety frameworks, and integration of multimodal clinical data. Through this survey, we seek to establish a roadmap for developing reliable LLMs that can serve as effective partners in clinical practice and medical research.