Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study

Zaifu Zhan; Mengyuan Cui; Rui Zhang

cs.CLMar 31, 2026

Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study

Authors: Zaifu Zhan, Mengyuan Cui, Rui Zhang

Organizations: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, USA · Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, USA · Department of Software Engineering, University of St. Thomas, Saint Paul,USA

Abstract

Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by eliciting explicit intermediate reasoning; meanwhile, self-reflective (self-corrective) prompting has been widely claimed to enhance model reliability by prompting LLMs to critique and revise their own reasoning, yet its effectiveness in safety-critical medical settings remains unclear. In this work, we conduct an exploratory analysis of self-reflective reasoning for medical multiple-choice question answering: using GPT-4o and GPT-4o-mini, we compare standard CoT prompting with an iterative self-reflection loop and track how predictions evolve across reflection steps on three widely used medical QA benchmarks (MedQA, HeadQA, and PubMedQA). We analyze whether self-reflection leads to error correction, error persistence, or the introduction of new errors. Our results show that self-reflective prompting does not consistently improve accuracy and its impact is highly dataset- and model-dependent: it yields modest gains on MedQA but provides limited or negative benefits on HeadQA and PubMedQA, and increasing the number of reflection steps does not guarantee better performance. These findings highlight a gap between reasoning transparency and reasoning correctness, suggesting that self-reflective reasoning is better viewed as an analytical tool for understanding model behavior rather than a standalone solution for improving medical QA reliability.

Figures & tables

Dataset	Test Set Size	Source
MedQA(USMLE)	1,273	US medical licensing examinations
HeadQA	244	Spanish medical specialization exams
PubMedQA	500	Biomedical literature (PubMed abstracts)

TABLE I: Datasets used in our study and their sources. We report only the test sets, as no training or fine-tuning is performed in this exploratory analysis.

Fig. 1: Overview of our self-reflective reasoning framework for medical multiple-choice question answering. Top: standard chain-of-thought (CoT) prompting, where the LLM produces an explicit rationale and a final option (A/B/C/D). Bottom: our self-reflective reasoning loop, which first generates an initial rationale–answer pair and then iteratively reviews the previous reasoning for medical or logical errors. If the reviewer is satisfied, the current answer is returned; otherwise, the model revises its rationale and updates the answer, repeating until convergence or a maximum number of reflection steps.

Fig. 2: Example prompt used for the MedQA dataset. The prompt instructs the model to generate a step-by-step clinical rationale followed by a final multiple-choice answer, and is extended with a structured self-reflection instruction for iterative reasoning analysis.

Fig. 3: Comparison of Chain-of-Thought reasoning and self-reflective reasoning accuracy on three medical QA datasets (HeadQA, MedQA, and PubMedQA). Results are reported for ChatGPT-4o and ChatGPT-4o-mini.

Fig. 4: Accuracy as a function of the number of cumulative self-reflection steps for ChatGPT-4o and ChatGPT-4o-mini across three datasets. Each curve shows how performance evolves as additional reflection steps are applied.

Fig. 5: Distribution of the number of self-reflection steps used by the models across all evaluation instances. Each pie chart shows the percentage of samples requiring a given number of reflection steps (0–10) for a specific dataset–model pair. Colors are consistent across charts to facilitate comparison.

Fig. 6: Average number of self-reflection steps used by ChatGPT-4o and ChatGPT-4o-mini on each dataset. Bars are colored by model, highlighting differences in reflection depth required across datasets.

LLM Self-Correction

Large language model self-correction studies how language models detect and repair errors in their own answers, reasoning, and actions.

See all 159 papers

Evaluating Small Open LLMs for Medical Question Answering: A Practical Framework
Apr 12, 2026Avi-ad Avraam BuskilaLLM Evaluation LLM Reliability
Department of Information Science and Applied Artificial Intelligence, Bar-Ilan University, Ramat-Gan, Israel
When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models
Mar 26, 2026Binesh Sadanandan, Vahid BehzadanPrompt Sensitivity LLM Prompting
SAIL Lab, University of New Haven, West Haven, CT 06516, USA
Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering
Jun 13, 2026Zaifu Zhan, Shuang Zhou, Rui ZhangMulti-Agent LLM Systems LLM Answer Verification
Department of Electrical and Computer Engineering, University of Minnesota, 200 Union St SE, 55455, Minneapolis, MN, United States · Division of Computational Health Sciences, Department of Surgery, University of Minnesota, 420 Delaware St SE, 55455, Minneapolis, MN, United States

Apr 12, 2026J/K move · Enter open · S save

Evaluating Small Open LLMs for Medical Question Answering: A Practical Framework

Avi-ad Avraam Buskila

Department of Information Science and Applied Artificial Intelligence, Bar-Ilan University, Ramat-Gan, Israel

LLM Evaluation LLM Reliability Clinical Language Model Evaluation Medical LLMs Biomedical QA

Open paper AskPDF arXiv

Incorporating large language models (LLMs) in medical question answering demands more than high average accuracy: a model that returns substantively different answers each time it is queried is not a reliable medical tool. Online health communities such as Reddit have become a primary source of medical information for millions of users, yet they remain highly susceptible to misinformation; deploying LLMs as assistants in these settings amplifies the need for output consistency alongside correctness. We present a practical, open-source evaluation framework for assessing small, locally-deployable open-weight LLMs on medical question answering, treating reproducibility as a first-class metric alongside lexical and semantic accuracy. Our pipeline computes eight quality metrics, including BERTScore, ROUGE-L, and an LLM-as-judge rubric, together with two within-model reproducibility metrics derived from repeated inference (N=10 runs per question). Evaluating three models (Llama 3.1 8B, Gemma 3 12B, MedGemma 1.5 4B) on 50 MedQuAD questions (N=1,500 total responses) reveals that despite low-temperature generation (T=0.2), self-agreement across runs reaches at most 0.20, while 87-97% of all outputs per model are unique -- a safety gap that single-pass benchmarks entirely miss. The clinically fine-tuned MedGemma 1.5 4B underperforms the larger general-purpose models on both quality and reproducibility; however, because MedGemma is also the smallest model, this comparison confounds domain fine-tuning with model scale. We describe the methodology in sufficient detail for practitioners to replicate or extend the evaluation for their own model-selection workflows. All code and data pipelines are available at https://github.com/aviad-buskila/llm_medical_reproducibility.

Abstract

LLM Self-Correction

Explore similar work

Evaluating Small Open LLMs for Medical Question Answering: A Practical Framework

When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models

Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering