Challenge: a survey of large language models in healthcare raises critical concerns around trustworthiness . trustworthy of LLMs in healthcare remains underexplored, lacking a systematic review .
Approach: a new survey examines the trustworthiness of large language models in healthcare . a review examines how each dimension affects reliability and ethical deployment of LLMs .
Outcome: The present study examines the trustworthiness of large language models in healthcare . it identifies key gaps in existing approaches and challenges posed by evolving paradigms .

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Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025.emnlp-main)

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Challenge: achieving comprehensive trustworthiness in medical QA poses significant challenges due to complexity of healthcare data, critical nature of clinical scenarios, and multifaceted dimensions of trustworthy AI.
Approach: They examine six key dimensions of trustworthiness in medical QA . they compare how each dimension is evaluated in existing LLM-based systems .
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Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

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Challenge: Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
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A Survey of LLM-based Agents in Medicine: How far are we from Baymax? (2025.findings-acl)

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Challenge: Large Language Models (LLMs) are transforming healthcare through their ability to understand and assist with medical tasks.
Approach: They analyze system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement.
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Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective (2025.coling-main)

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Challenge: Existing large language models (LLMs) are not effective in solving real-world healthcare tasks, but they are able to provide demographic information and provide biased health predictions.
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Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation (2025.acl-industry)

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Challenge: Clinical note generation (CNG) tools are being developed to address extended working hours and healthcare provider fatigue.
Approach: They evaluate the reliability of 12 open-weight and proprietary LLMs from Anthropic, Meta, Mistral, and OpenAI in CNG in terms of their ability to generate notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (symbol similarity)
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Safety of Large Language Models Beyond English: A Systematic Literature Review of Risks, Biases, and Safeguards (2026.eacl-long)

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Challenge: Large language models (LLMs) have a growing number of applications that generate harmful, biased, or unsafe content.
Approach: They synthesize findings from recent studies that evaluate their robustness across languages . they highlight gaps in multilingual safety research and recommend future work .
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When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation (2026.eacl-long)

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Challenge: Existing benchmarks for large language models are limited in scale, authenticity, and reliability due to the emotionally complex nature of therapeutic dialogue.
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From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations (2025.emnlp-main)

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Challenge: Existing benchmarks assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments.
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A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains.
Approach: They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks .
Outcome: The proposed evaluations are reproducible, reliable, and robust.
MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics (2025.naacl-industry)

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Challenge: Large language models (LLMs) have been used in clinical decision support, medical education and patient communication.
Approach: They propose a benchmark to evaluate large language models in the domain of medical ethics and assess their grasp of medical ethical principles and their application across diverse scenarios.
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