mlsanityData to 14 Oct 2026

A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions

arXiv 2406.03712 ↗First posted 6 Jun 2024cs.CL cs.LGcited by 34

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Abstract (arXiv)

With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-purpose models to medical-specialized applications. Firstly, we delve into the foundational technology of Med-LLMs, indicating how general models can be progressively adapted and refined for the complicated medical tasks. Secondly, the wide-ranging applications of Med-LLMs are investigated across various healthcare domains, as well as an up-to-date review of existing Med-LLMs. The transformative impact of these models on daily medical practice is evident through their ability to assist clinicians, educators, and patients. Recognizing the importance of responsible innovation, we discuss the challenges associated with ensuring fairness, accountability, privacy, and robustness. Ethical considerations, rigorous evaluation methodologies, and the establishment of regulatory frameworks are crucial for building trustworthiness in the real-world system. We emphasize the need for ongoing scrutiny and development to maintain high standards of safety and reliability. Finally, we anticipate possible future trajectories for Med-LLMs, identifying key avenues for prudent expansion. By consolidating these insights, our review aims to provide professionals and researchers with a thorough understanding of the strengths and limitations of Med-LLMs, fostering a balanced and ethical approach to their integration into the healthcare ecosystem.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 158 of its 266 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

158 of 266 references.

References · 158

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 19; 2026: 12.

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  1. 2025Free-MAD: Consensus-Free Multi-Agent Debatecited by 29
  2. 2025MMInference: Accelerating Pre-filling for Long-Context VLMs via Modality-Aware Permutation Sparse Attentioncited by 26
  3. 2025SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thoughtcited by 13
  4. 2025MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibilitycited by 9
  5. 2026CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoningcited by 9
  6. 2025The Multi-Round Diagnostic RAG Framework for Emulating Clinical Reasoningcited by 8
  7. 2025Enhancing Large Language Model Reasoning with Reward Models: An Analytical Surveycited by 8
  8. 2025Are Large Language Models Sensitive to the Motives Behind Communication?cited by 8
  9. 2025Reasoning LLMs in the Medical Domain: A Literature Surveycited by 6
  10. 2025BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generationcited by 4
  11. 2024OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomographycited by 3
  12. 2025SEAT: Sparse Entity-Aware Tuning for Knowledge Adaptation while Preserving Epistemic Abstentioncited by 3
22 more
  1. 2025MedReason-R1: Learning to Reason for CT Diagnosis with Reinforcement Learning and Local Zoomcited by 3
  2. 2025CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generationcited by 3
  3. 2026Improving the Safety and Trustworthiness of Medical AI via Multi-Agent Evaluation Loopscited by 3
  4. 2024DALL-M: Context-Aware Clinical Data Augmentation with LLMscited by 2
  5. 2025Knowledge-Augmented Language Models Interpreting Structured Chest X-Ray Findingscited by 2
  6. 2025TERAG: Token-Efficient Graph-Based Retrieval-Augmented Generationcited by 2
  7. 2026LLM is Not All You Need: A Systematic Evaluation of ML vs. Foundation Models for text and image based Medical Classificationcited by 2
  8. 2024Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Modelscited by 1
  9. 2025AnesSuite: A Comprehensive Benchmark and Dataset Suite for Anesthesiology Reasoning in LLMscited by 1
  10. 2025QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Modelcited by 1
  11. 2026Toward Workflow-Aware Benchmarking for Healthcare NLP Agentscited by 1
  12. 2025Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Modelscited by 0
  13. 2025NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detectioncited by 0
  14. 2025SparseDoctor: Towards Efficient Chat Doctor with Mixture of Experts Enhanced Large Language Modelscited by 0
  15. 2026Babel: Jailbreaking Safety Attention via Obfuscation Distribution Optimized Samplingcited by 0
  16. 2026Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Modelscited by 0
  17. 2026ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoningcited by 0
  18. 2026Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Datacited by 0
  19. 2026Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQAcited by 0
  20. 2026Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studiescited by 0
  21. 2026LingLan: An Advancing Traditional Chinese Medicine Diagnosis LLM with Multimodal Datacited by 0
  22. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0