Evaluating Large Language Models on Medical Evidence Summarization
By Liyan Tang, Z. Sun, B. Idnay, J. Nestor, A. Soroush, P. A. Elias, Z. Xu, Y. Ding, Greg Durrett, Justin F. Rousseau and 2 more. medRxiv, 2023
459 citations recorded by the source corpus.
Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study demonstrates that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts.
Subfields: Artificial Intelligence, Health Informatics
View Paper: https://www.semanticscholar.org/paper/23b8dd1978dd2d27e5ab4dc4a8fe11ea6dd57064
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In Artificial Intelligence
Citations in the corpus
- Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health (2023, background): “Patel and Lam [89] discussed the possibility of using an LLM to generate discharge summaries, and Tang et al. [90] tested performance of ChatGPT on their in-house medical evidence dataset.”
- Large language models in healthcare and medical domain: A review (2023, background): “In a fast-evolving field like healthcare, the ability to maintain currency with the latest advancements is paramount, and LLMs can play a pivotal role in ensuring that healthcare remains at the forefront of innovation and evidence-based care delivery [73, 74].”
- The future landscape of large language models in medicine (2023, background): “5 (Box 2, Supplementary Data, example 2, 5, 10), LLMs currently do not always provide appropriate detailed summaries or critical appraisals of up-to-date, high-quality, peer-reviewed evidence 50 .”
- A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation (2025, unknown): “Interestingly, they assessed the clinician ’ s preference for different outputs 35 .”