mlsanityData to 7 Oct 2026

A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare

arXiv 2502.15871 ↗First posted 21 Feb 2025cs.CY cs.AI cs.CLcited by 27

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Abstract (arXiv)

The application of large language models (LLMs) in healthcare holds significant promise for enhancing clinical decision-making, medical research, and patient care. However, their integration into real-world clinical settings raises critical concerns around trustworthiness, particularly around dimensions of truthfulness, privacy, safety, robustness, fairness, and explainability. These dimensions are essential for ensuring that LLMs generate reliable, unbiased, and ethically sound outputs. While researchers have recently begun developing benchmarks and evaluation frameworks to assess LLM trustworthiness, the trustworthiness of LLMs in healthcare remains underexplored, lacking a systematic review that provides a comprehensive understanding and future insights. This survey addresses that gap by providing a comprehensive review of current methodologies and solutions aimed at mitigating risks across key trust dimensions. We analyze how each dimension affects the reliability and ethical deployment of healthcare LLMs, synthesize ongoing research efforts, and identify critical gaps in existing approaches. We also identify emerging challenges posed by evolving paradigms, such as multi-agent collaboration, multi-modal reasoning, and the development of small open-source medical models. Our goal is to guide future research toward more trustworthy, transparent, and clinically viable LLMs.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 34 of its 92 references that are in the map. First posted on arXiv 21 Feb 2025.

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34 of 92 references.

References · 34

  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  2. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  3. 2021Editing Factual Knowledge in Language Modelscited by 548
  4. 2023MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Datacited by 282
  5. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 246
  6. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
  7. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 156
  8. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 155
  9. 2023DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4cited by 128
  10. 2023DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Modelscited by 124
  11. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  12. 2024On Protecting the Data Privacy of Large Language Models (LLMs): A Surveycited by 91
22 more
  1. 2023AlpaCare:Instruction-tuned Large Language Models for Medical Applicationcited by 88
  2. 2020Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddingscited by 79
  3. 2024MedExQA: Medical Question Answering Benchmark with Multiple Explanationscited by 58
  4. 2024FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Modelscited by 47
  5. 2023Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AIcited by 33
  6. 2024Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generationcited by 31
  7. 2023A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosiscited by 30
  8. 2024MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answeringcited by 26
  9. 2024Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attackscited by 25
  10. 2024MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applicationscited by 25
  11. 2024Open (Clinical) LLMs are Sensitive to Instruction Phrasingscited by 23
  12. 2024Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarkscited by 17
  13. 2024MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationcited by 17
  14. 2024MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationalecited by 14
  15. 2025Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMscited by 12
  16. 2024Adversarial Attacks on Large Language Models in Medicinecited by 9
  17. 2024Improving Black-box Robustness with In-Context Rewritingcited by 8
  18. 2024Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Researchcited by 8
  19. 2024MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialoguescited by 8
  20. 2023Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthinesscited by 3
  21. 2024Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Seriescited by 2
  22. 2025Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 14; 2026: 13.

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  1. 2025The Rise of Small Language Models in Healthcare: A Comprehensive Surveycited by 18
  2. 2025Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metricscited by 12
  3. 2025OralGPT-Omni: A Versatile Dental Multimodal Large Language Modelcited by 10
  4. 2025Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learningcited by 9
  5. 2026Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoningcited by 9
  6. 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Taskscited by 7
  7. 2025Trustworthy Medical Question Answering: An Evaluation-Centric Surveycited by 7
  8. 2025PsychiatryBench: A Multi-Task Benchmark for LLMs in Psychiatrycited by 6
  9. 2026On Calibration of Large Language Models: From Response To Capabilitycited by 5
  10. 2025Explaining Large Language Models with gSMILEcited by 4
  11. 2026Aletheia: What Makes RLVR For Code Verifiers Tick?cited by 4
  12. 2026When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressurecited by 4
15 more
  1. 2025From Learning to Unlearning: Biomedical Security Protection in Multimodal Large Language Modelscited by 3
  2. 2026SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflowcited by 3
  3. 2025Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systemscited by 2
  4. 2025ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agentscited by 2
  5. 2026ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citationscited by 2
  6. 2026SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendationscited by 1
  7. 2026Domain-Adapted Small Language Models for Reliable Clinical Triagecited by 1
  8. 2026Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMscited by 1
  9. 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
  10. 2025Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challengescited by 0
  11. 2025A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programmingcited by 0
  12. 2026Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMscited by 0
  13. 2026Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Modelscited by 0
  14. 2026PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Modelscited by 0
  15. 2026Toward Workflow-Aware Benchmarking for Healthcare NLP Agentscited by 0