Summary of Large Language Model Benchmarks in Medical Tasks, by Lawrence K.q. Yan et al.
Large Language Model Benchmarks in Medical Tasks
by Lawrence K.Q. Yan, Qian Niu, Ming Li, Yichao Zhang, Caitlyn Heqi Yin, Cheng Fei, Benji Peng, Ziqian Bi, Pohsun Feng, Keyu Chen, Tianyang Wang, Yunze Wang, Silin Chen, Ming Liu, Junyu Liu
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive survey of benchmark datasets used to evaluate large language models (LLMs) in medical applications. The study categorizes datasets by modality, including text, image, and multimodal benchmarks, which focus on different aspects of medical knowledge such as electronic health records, doctor-patient dialogues, and medical image captioning. Key benchmarks include MIMIC-III, MIMIC-IV, BioASQ, PubMedQA, and CheXpert, which have facilitated advancements in tasks like medical report generation, clinical summarization, and synthetic data generation. The paper highlights the need for datasets with greater language diversity, structured omics data, and innovative approaches to synthesis. This work provides a foundation for future research on applying LLMs in medicine, contributing to the evolving field of medical artificial intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how large language models are used in medicine and how they’re tested using special datasets. These datasets come in different forms, like text, images, or a mix of both. They cover things like patient records, conversations between doctors and patients, and even writing descriptions of medical images. The study focuses on some important benchmarks that help make progress in tasks like writing medical reports, summarizing patient data, and creating fake data. It’s all about helping machines learn more about medicine. |
Keywords
» Artificial intelligence » Image captioning » Summarization » Synthetic data
