# Large Language Models for Disease Diagnosis: A Scoping Review

arXiv: https://arxiv.org/abs/2409.00097 · HTML: https://mlsanity.com/paper/2409.00097/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

## Built on by (most cited first, 30 of 51)

- [Dynamic and Generalizable Process Reward Modeling](https://mlsanity.com/paper/2507.17849/) (2025)
- [MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning](https://mlsanity.com/paper/2502.15954/) (2025)
- [A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs](https://mlsanity.com/paper/2504.14657/) (2025)
- [FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets](https://mlsanity.com/paper/2505.19819/) (2025)
- [KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs](https://mlsanity.com/paper/2507.02773/) (2025)
- [RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements](https://mlsanity.com/paper/2411.15700/) (2024)
- [End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning](https://mlsanity.com/paper/2508.15746/) (2025)
- [Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support](https://mlsanity.com/paper/2506.06340/) (2025)
- [Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree](https://mlsanity.com/paper/2508.03038/) (2025)
- [An evaluation of DeepSeek Models in Biomedical Natural Language Processing](https://mlsanity.com/paper/2503.00624/) (2025)
- [MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM](https://mlsanity.com/paper/2509.20067/) (2025)
- [Retrieval-augmented in-context learning for multimodal large language models in disease classification](https://mlsanity.com/paper/2505.02087/) (2025)
- [Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications](https://mlsanity.com/paper/2509.08604/) (2025)
- [Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning](https://mlsanity.com/paper/2603.27820/) (2026)
- [AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity](https://mlsanity.com/paper/2505.23520/) (2025)
- [Knowing What You Know Is Not Enough: Large Language Model Confidences Don't Align With Their Actions](https://mlsanity.com/paper/2511.13240/) (2025)
- [Large Language models for Time Series Analysis: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2506.11040/) (2025)
- [Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation](https://mlsanity.com/paper/2509.04534/) (2025)
- [Generalist Foundation Models Are Not Clinical Enough for Hospital Operations](https://mlsanity.com/paper/2511.13703/) (2025)
- [Distilling the Thought, Watermarking the Answer: A Principle Semantic Guided Watermark for Large Reasoning Models](https://mlsanity.com/paper/2601.05144/) (2026)
- [Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson's Disease](https://mlsanity.com/paper/2502.15069/) (2025)
- [DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration](https://mlsanity.com/paper/2505.18630/) (2025)
- [Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models](https://mlsanity.com/paper/2511.12008/) (2025)
- [Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?](https://mlsanity.com/paper/2605.28778/) (2026)
- [Explainable AI for Mental Health Emergency Returns: Integrating LLMs with Predictive Modeling](https://mlsanity.com/paper/2502.00025/) (2025)
- [Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond](https://mlsanity.com/paper/2502.20988/) (2025)
- [Refine Medical Diagnosis Using Generation Augmented Retrieval and Clinical Practice Guidelines](https://mlsanity.com/paper/2506.21615/) (2025)
- [Trustworthy Reasoning: Evaluating and Enhancing Factual Accuracy in LLM Intermediate Thought Processes](https://mlsanity.com/paper/2507.22940/) (2025)
- [DKG-LLM : A Framework for Medical Diagnosis and Personalized Treatment Recommendations via Dynamic Knowledge Graph and Large Language Model Integration](https://mlsanity.com/paper/2508.06186/) (2025)
- [Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective](https://mlsanity.com/paper/2508.08140/) (2025)

## Abstract (arXiv)

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.