# Trustworthy Medical Question Answering: An Evaluation-Centric Survey

arXiv: https://arxiv.org/abs/2506.03659 · HTML: https://mlsanity.com/paper/2506.03659/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 4 Jun 2025.

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

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [Calibration of Pre-trained Transformers](https://mlsanity.com/paper/2003.07892/) (2020)
- [MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback](https://mlsanity.com/paper/2309.10691/) (2023)
- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://mlsanity.com/paper/2307.15343/) (2023)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2005.00646/) (2020)
- [Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue](https://mlsanity.com/paper/2308.03549/) (2023)
- [MedExQA: Medical Question Answering Benchmark with Multiple Explanations](https://mlsanity.com/paper/2406.06331/) (2024)
- [RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning](https://mlsanity.com/paper/2401.08326/) (2024)
- [MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering](https://mlsanity.com/paper/2404.05590/) (2024)
- [Addressing cognitive bias in medical language models](https://mlsanity.com/paper/2402.08113/) (2024)
- [A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare](https://mlsanity.com/paper/2502.15871/) (2025)
- [MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering](https://mlsanity.com/paper/2406.06573/) (2024)
- [QA-Calibration of Language Model Confidence Scores](https://mlsanity.com/paper/2410.06615/) (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)
- [Uncertainty Estimation of Large Language Models in Medical Question Answering](https://mlsanity.com/paper/2407.08662/) (2024)
- [Improving the robustness and accuracy of biomedical language models through adversarial training](https://mlsanity.com/paper/2111.08529/) (2021)
- [Fine-Tuning LLMs for Reliable Medical Question-Answering Services](https://mlsanity.com/paper/2410.16088/) (2024)
- [Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots](https://mlsanity.com/paper/2412.04235/) (2024)
- [Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models](https://mlsanity.com/paper/2503.18562/) (2025)
- [Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks](https://mlsanity.com/paper/2402.10527/) (2024)

## Built on by (most cited first, 7 of 7)

- [Balancing Safety and Helpfulness in Healthcare AI Assistants through Iterative Preference Alignment](https://mlsanity.com/paper/2512.04210/) (2025)
- [Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs](https://mlsanity.com/paper/2601.15429/) (2026)
- [Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response](https://mlsanity.com/paper/2601.19918/) (2026)
- [Quantifying Hallucinations in Language Language Models on Medical Textbooks](https://mlsanity.com/paper/2603.09986/) (2026)
- [SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning](https://mlsanity.com/paper/2608.22132/) (2026)
- [PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning](https://mlsanity.com/paper/2609.04598/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)

## Abstract (arXiv)

Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence. As large language models (LLMs) become increasingly integrated into medical settings, the reliability of their responses directly influences clinical decision-making and patient outcomes. However, achieving comprehensive trustworthiness in medical QA poses significant challenges due to the inherent complexity of healthcare data, the critical nature of clinical scenarios, and the multifaceted dimensions of trustworthy AI. In this survey, we systematically examine six key dimensions of trustworthiness in medical QA, i.e., Factuality, Robustness, Fairness, Safety, Explainability, and Calibration. We review how each dimension is evaluated in existing LLM-based medical QA systems. We compile and compare major benchmarks designed to assess these dimensions and analyze evaluation-guided techniques that drive model improvements, such as retrieval-augmented grounding, adversarial fine-tuning, and safety alignment. Finally, we identify open challenges-such as scalable expert evaluation, integrated multi-dimensional metrics, and real-world deployment studies-and propose future research directions to advance the safe, reliable, and transparent deployment of LLM-powered medical QA.