# Automatic Replication of LLM Mistakes in Medical Conversations

arXiv: https://arxiv.org/abs/2512.20983 · HTML: https://mlsanity.com/paper/2512.20983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks](https://mlsanity.com/paper/2505.23802/) (2025)
- [MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models](https://mlsanity.com/paper/2403.03744/) (2024)
- [Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning](https://mlsanity.com/paper/2406.10834/) (2024)
- [Generating Valid and Natural Adversarial Examples with Large Language Models](https://mlsanity.com/paper/2311.11861/) (2023)
- [MedPI: Evaluating AI Systems in Medical Patient-facing Interactions](https://mlsanity.com/paper/2601.04195/) (2025)
- [Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models](https://mlsanity.com/paper/2507.10934/) (2025)

## Built on by (most cited first, 1 of 1)

- [The Consistency Dilemma in LLMs: Generator-Evaluator Agreement and Vulnerability to Mistakes](https://mlsanity.com/paper/2606.30653/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly evaluated in clinical settings using multi-dimensional rubrics which quantify reasoning quality, safety, and patient-centeredness. Yet, replicating specific mistakes in other LLM models is not straightforward and often requires manual effort. We introduce MedMistake, an automatic pipeline that extracts mistakes LLMs make in patient-doctor conversations and converts them into a benchmark of single-shot QA pairs. Our pipeline (1) creates complex, conversational data between an LLM patient and LLM doctor, (2) runs an evaluation with a committee of 2 LLM judges across a variety of dimensions and (3) creates simplified single-shot QA scenarios from those mistakes. We release MedMistake-All, a dataset of 3,390 single-shot QA pairs where GPT-5 and Gemini 2.5 Pro are currently failing to answer correctly, as judged by two LLM judges. We used medical experts to validate a subset of 211/3390 questions (MedMistake-Bench), which we used to run a final evaluation of 12 frontier LLMs: Claude Opus 4.5, Claude Sonnet 4.5, DeepSeek-Chat, Gemini 2.5 Pro, Gemini 3 Pro, GPT-4o, GPT-5, GPT-5.1, GPT-5.2, Grok 4, Grok 4.1, Mistral Large. We found that GPT models, Claude and Grok obtained the best performance on MedMistake-Bench. We release both the doctor-validated benchmark (MedMistake-Bench), as well as the full dataset (MedMistake-All) at https://huggingface.co/datasets/TheLumos/MedicalMistakeBenchmark.