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

arXiv: https://arxiv.org/abs/2502.15871 · HTML: https://mlsanity.com/paper/2502.15871/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 92 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 30 of 34)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data](https://mlsanity.com/paper/2304.08247/) (2023)
- [Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs](https://mlsanity.com/paper/2406.15927/) (2024)
- [Security and Privacy Challenges of Large Language Models: A Survey](https://mlsanity.com/paper/2402.00888/) (2024)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://mlsanity.com/paper/2307.15343/) (2023)
- [LM vs LM: Detecting Factual Errors via Cross Examination](https://mlsanity.com/paper/2305.13281/) (2023)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models](https://mlsanity.com/paper/2310.16436/) (2023)
- [Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations](https://mlsanity.com/paper/2306.04618/) (2023)
- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [AlpaCare:Instruction-tuned Large Language Models for Medical Application](https://mlsanity.com/paper/2310.14558/) (2023)
- [Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings](https://mlsanity.com/paper/2003.11515/) (2020)
- [MedExQA: Medical Question Answering Benchmark with Multiple Explanations](https://mlsanity.com/paper/2406.06331/) (2024)
- [FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models](https://mlsanity.com/paper/2407.00983/) (2024)
- [Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI](https://mlsanity.com/paper/2311.01463/) (2023)
- [A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis](https://mlsanity.com/paper/2307.01981/) (2023)
- [Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation](https://mlsanity.com/paper/2401.13867/) (2024)
- [Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks](https://mlsanity.com/paper/2402.11638/) (2024)
- [MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering](https://mlsanity.com/paper/2406.06573/) (2024)
- [MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications](https://mlsanity.com/paper/2409.07314/) (2024)
- [Open (Clinical) LLMs are Sensitive to Instruction Phrasings](https://mlsanity.com/paper/2407.09429/) (2024)
- [Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks](https://mlsanity.com/paper/2406.12066/) (2024)
- [MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation](https://mlsanity.com/paper/2409.19684/) (2024)
- [MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale](https://mlsanity.com/paper/2404.12372/) (2024)
- [Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs](https://mlsanity.com/paper/2501.14892/) (2025)
- [Adversarial Attacks on Large Language Models in Medicine](https://mlsanity.com/paper/2406.12259/) (2024)
- [Geneverse: A collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research](https://mlsanity.com/paper/2406.15534/) (2024)
- [Improving Black-box Robustness with In-Context Rewriting](https://mlsanity.com/paper/2402.08225/) (2024)

## Built on by (most cited first, 27 of 27)

- [The Rise of Small Language Models in Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2504.17119/) (2025)
- [Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics](https://mlsanity.com/paper/2505.18658/) (2025)
- [OralGPT-Omni: A Versatile Dental Multimodal Large Language Model](https://mlsanity.com/paper/2511.22055/) (2025)
- [Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.15279/) (2025)
- [Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoning](https://mlsanity.com/paper/2603.01326/) (2026)
- [Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks](https://mlsanity.com/paper/2504.13199/) (2025)
- [Trustworthy Medical Question Answering: An Evaluation-Centric Survey](https://mlsanity.com/paper/2506.03659/) (2025)
- [On Calibration of Large Language Models: From Response To Capability](https://mlsanity.com/paper/2602.13540/) (2026)
- [PsychiatryBench: A Multi-Task Benchmark for LLMs in Psychiatry](https://mlsanity.com/paper/2509.09711/) (2025)
- [When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure](https://mlsanity.com/paper/2605.23932/) (2026)
- [Aletheia: What Makes RLVR For Code Verifiers Tick?](https://mlsanity.com/paper/2601.12186/) (2026)
- [Explaining Large Language Models with gSMILE](https://mlsanity.com/paper/2505.21657/) (2025)
- [From Learning to Unlearning: Biomedical Security Protection in Multimodal Large Language Models](https://mlsanity.com/paper/2508.04192/) (2025)
- [SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations](https://mlsanity.com/paper/2601.10004/) (2026)
- [SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow](https://mlsanity.com/paper/2605.29368/) (2026)
- [Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems](https://mlsanity.com/paper/2506.05370/) (2025)
- [ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents](https://mlsanity.com/paper/2510.16381/) (2025)
- [ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations](https://mlsanity.com/paper/2605.00846/) (2026)
- [Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs](https://mlsanity.com/paper/2605.30646/) (2026)
- [Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs](https://mlsanity.com/paper/2601.15714/) (2026)
- [Domain-Adapted Small Language Models for Reliable Clinical Triage](https://mlsanity.com/paper/2604.26766/) (2026)
- [PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models](https://mlsanity.com/paper/2606.25442/) (2026)
- [Toward Workflow-Aware Benchmarking for Healthcare NLP Agents](https://mlsanity.com/paper/2609.00296/) (2026)
- [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025)
- [Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges](https://mlsanity.com/paper/2511.22483/) (2025)
- [A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming](https://mlsanity.com/paper/2512.23932/) (2025)
- [Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models](https://mlsanity.com/paper/2604.08815/) (2026)

## 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.