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Summary of Does Biomedical Training Lead to Better Medical Performance?, by Amin Dada et al.


Does Biomedical Training Lead to Better Medical Performance?

by Amin Dada, Marie Bauer, Amanda Butler Contreras, Osman Alperen Koraş, Constantin Marc Seibold, Kaleb E Smith, Jens Kleesiek

First submitted to arxiv on: 5 Apr 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

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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
The study investigates the performance of large language models (LLMs) in medical tasks, assessing their suitability for biomedical applications. Specifically, it evaluates 25 LLMs on six practical medical tasks, including hallucinations, ICD10 coding, and instruction adherence. The results show a decline in performance in nine out of twelve biomedical models after fine-tuning, suggesting a trade-off between domain-specific fine-tuning and general medical task performance. Notably, general-domain models like Meta-Llama-3.1-70B-Instruct outperformed their biomedical counterparts. This study highlights the need for systematic evaluation of LLMs in medical tasks and provides open-source evaluation scripts and datasets to support further research.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper looks at how well large language models (LLMs) do on medical tasks like diagnosing patients or doing administrative work. It wants to know if these models are good enough for real-life use in healthcare. The study tests 25 different LLMs on six important medical tasks, and it finds that some of the models don’t get better at their jobs even when they’re fine-tuned just for medicine. Actually, some general-use models do a better job than ones specifically trained for medicine. This research is important because it helps us understand what these language models can really do in healthcare.

Keywords

» Artificial intelligence  » Fine tuning  » Llama