Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
Burcu Sayin, Pasquale Minervini, Jacopo Staiano, Andrea Passerini
Abstract
We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks. We evaluate several LLMs, including Meditron, Llama2, and Mistral, to analyze the ability of these models to interact effectively with physicians across different scenarios. We consider questions from PubMedQA and several tasks, ranging from binary (yes/no) responses to long answer generation, where the answer of the model is produced after an interaction with a physician. Our findings suggest that prompt design significantly influences the downstream accuracy of LLMs and that LLMs can provide valuable feedback to physicians, challenging incorrect diagnoses and contributing to more accurate decision-making. For example, when the physician is accurate 38% of the time, Mistral can produce the correct answer, improving accuracy up to 74% depending on the prompt being used, while Llama2 and Meditron models exhibit greater sensitivity to prompt choice. Our analysis also uncovers the challenges of ensuring that LLM-generated suggestions are pertinent and useful, emphasizing the need for further research in this area.- Anthology ID:
- 2024.clinicalnlp-1.19
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 218–237
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.19
- DOI:
- Cite (ACL):
- Burcu Sayin, Pasquale Minervini, Jacopo Staiano, and Andrea Passerini. 2024. Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 218–237, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain (Sayin et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.19.pdf