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[2602.22483] Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models
Summary
This paper discusses the significance of prompt optimization in enhancing error detection in medical notes using language models, demonstrating improved accuracy through rigorous experiments.
Why It Matters
The ability to accurately detect errors in medical documentation is crucial for patient safety and treatment efficacy. This research highlights how optimizing prompts can significantly enhance the performance of language models, potentially transforming healthcare systems by reducing errors.
Key Takeaways
- Prompt optimization can improve error detection accuracy in medical notes.
- The study shows a performance increase from 0.669 to 0.785 using GPT-5.
- Automatic prompt optimization approaches state-of-the-art performance on the MEDEC benchmark dataset.
- Error detection capabilities of language models can rival those of medical professionals.
- The research includes rigorous experiments across various language models.
Computer Science > Computation and Language arXiv:2602.22483 (cs) [Submitted on 25 Feb 2026] Title:Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models Authors:Craig Myles, Patrick Schrempf, David Harris-Birtill View a PDF of the paper titled Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models, by Craig Myles and 2 other authors View PDF HTML (experimental) Abstract:Errors in medical text can cause delays or even result in incorrect treatment for patients. Recently, language models have shown promise in their ability to automatically detect errors in medical text, an ability that has the opportunity to significantly benefit healthcare systems. In this paper, we explore the importance of prompt optimisation for small and large language models when applied to the task of error detection. We perform rigorous experiments and analysis across frontier language models and open-source language models. We show that automatic prompt optimisation with Genetic-Pareto (GEPA) improves error detection over the baseline accuracy performance from 0.669 to 0.785 with GPT-5 and 0.578 to 0.690 with Qwen3-32B, approaching the performance of medical doctors and achieving state-of-the-art performance on the MEDEC benchmark dataset. Code available on GitHub: this https URL Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22483 [cs.CL] (or arXiv:2602.22483v1 [...
