# Evaluating large language models in medical applications: a survey

arXiv: https://arxiv.org/abs/2405.07468 · HTML: https://mlsanity.com/paper/2405.07468/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 2 of 2)

- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)

## Built on by (most cited first, 8 of 8)

- [EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis](https://mlsanity.com/paper/2409.06644/) (2024)
- [HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection](https://mlsanity.com/paper/2502.11948/) (2025)
- [DeepSeek-R1 Outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in Bilingual Complex Ophthalmology Reasoning](https://mlsanity.com/paper/2502.17947/) (2025)
- [Visual Question Answering in Ophthalmology: A Progressive and Practical Perspective](https://mlsanity.com/paper/2410.16662/) (2024)
- [Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines](https://mlsanity.com/paper/2601.03627/) (2026)
- [Toward Workflow-Aware Benchmarking for Healthcare NLP Agents](https://mlsanity.com/paper/2609.00296/) (2026)
- [LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents](https://mlsanity.com/paper/2509.02241/) (2025)
- [Mirror, Mirror on the Wall -- Which is the Best Model of Them All?](https://mlsanity.com/paper/2512.02043/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information. This paper provides a comprehensive overview of the landscape of medical LLM evaluation, synthesizing insights from existing studies and highlighting evaluation data sources, task scenarios, and evaluation methods. Additionally, it identifies key challenges and opportunities in medical LLM evaluation, emphasizing the need for continued research and innovation to ensure the responsible integration of LLMs into clinical practice.