mlsanityData to 7 Oct 2026

Large language models in healthcare and medical domain: A review

arXiv 2401.06775 ↗First posted 12 Dec 2023cs.CL cs.AIcited by 126

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Abstract (arXiv)

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable capability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into the functionalities of existing LLMs designed for healthcare applications, elucidating the trajectory of their development, starting from traditional Pretrained Language Models (PLMs) to the present state of LLMs in healthcare sector. First, we explore the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications, particularly focusing on clinical language understanding tasks. These tasks encompass a wide spectrum, ranging from named entity recognition and relation extraction to natural language inference, multi-modal medical applications, document classification, and question-answering. Additionally, we conduct an extensive comparison of the most recent state-of-the-art LLMs in the healthcare domain, while also assessing the utilization of various open-source LLMs and highlighting their significance in healthcare applications. Furthermore, we present the essential performance metrics employed to evaluate LLMs in the biomedical domain, shedding light on their effectiveness and limitations. Finally, we summarize the prominent challenges and constraints faced by large language models in the healthcare sector, offering a holistic perspective on their potential benefits and shortcomings. This review provides a comprehensive exploration of the current landscape of LLMs in healthcare, addressing their role in transforming medical applications and the areas that warrant further research and development.

Key facts. Cited by 126 papers in the map (data through 7 Oct 2026). Builds on 70 of its 135 references that are in the map. First posted on arXiv 12 Dec 2023.

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70 of 135 references.

References · 70

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 22; 2025: 73; 2026: 31.

126 papers.

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  1. 2024A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challengescited by 55
  2. 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating Itcited by 42
  3. 2024LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Modelscited by 31
  4. 2024Mitigating the Risk of Health Inequity Exacerbated by Large Language Modelscited by 21
  5. 2025FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answeringcited by 20
  6. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  7. 2024Efficient Fine-Tuning of Large Language Models for Automated Medical Documentationcited by 16
  8. 2025MedArabiQ: Benchmarking Large Language Models on Arabic Medical Taskscited by 15
  9. 2025CacheClip: Accelerating RAG with Effective KV Cache Reusecited by 14
  10. 2024The Sociolinguistic Foundations of Language Modelingcited by 13
  11. 2025Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledgecited by 12
  12. 2024Look Within, Why LLMs Hallucinate: A Causal Perspectivecited by 11
38 more
  1. 2025Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameterscited by 11
  2. 2025Gatekeeper: Improving Model Cascades Through Confidence Tuningcited by 10
  3. 2025LExT: Towards Evaluating Trustworthiness of Natural Language Explanationscited by 9
  4. 2024Polish-English medical knowledge transfer: A new benchmark and resultscited by 8
  5. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  6. 2025MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systemscited by 8
  7. 2024CLIMB: A Benchmark of Clinical Bias in Large Language Modelscited by 7
  8. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  9. 2025Trustworthy Medical Question Answering: An Evaluation-Centric Surveycited by 7
  10. 2025Shallow Robustness, Deep Vulnerabilities: Multi-Turn Evaluation of Medical LLMscited by 7
  11. 2024The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notescited by 6
  12. 2024PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM Ensemblescited by 6
  13. 2025Susceptibility of Large Language Models to User-Driven Factors in Medical Queriescited by 6
  14. 2025FairMedQA: Benchmarking Bias in Large Language Models for Medical Question Answeringcited by 6
  15. 2024Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimizationcited by 5
  16. 2024Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcarecited by 4
  17. 2025Electronic Circuit Principles of Large Language Modelscited by 4
  18. 2025An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimationcited by 4
  19. 2025MedM-VL: What Makes a Good Medical LVLM?cited by 4
  20. 2025Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detectioncited by 4
  21. 2025MIRA: A Novel Framework for Fusing Modalities in Medical RAGcited by 4
  22. 2025Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Searchcited by 3
  23. 2025Can Reasoning LLMs Enhance Clinical Document Classification?cited by 3
  24. 2025SoK: Semantic Privacy in Large Language Modelscited by 3
  25. 2025Street-Level AI: Are Large Language Models Ready for Real-World Judgments?cited by 3
  26. 2025Arabic Large Language Models for Medical Text Generationcited by 3
  27. 2026ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplificationcited by 3
  28. 2026The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applicationscited by 3
  29. 2024A critical review of methods and challenges in large language modelscited by 2
  30. 2024XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcarecited by 2
  31. 2024Exploring Language Model Generalization in Low-Resource Extractive QAcited by 2
  32. 2025Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncologycited by 2
  33. 2025Can Large Language Models Capture Video Game Engagement?cited by 2
  34. 2025Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoringcited by 2
  35. 2025Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPAcited by 2
  36. 2025A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMscited by 2
  37. 2025LLM-Driven Treatment Effect Estimation Under Inference Time Text Confoundingcited by 2
  38. 2025RephQA: Evaluating Readability of Large Language Models in Public Health Question Answeringcited by 2

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