Summary of Retrospective Comparative Analysis Of Prostate Cancer In-basket Messages: Responses From Closed-domain Llm Vs. Clinical Teams, by Yuexing Hao et al.
Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams
by Yuexing Hao, Jason M. Holmes, Jared Hobson, Alexandra Bennett, Daniel K. Ebner, David M. Routman, Satomi Shiraishi, Samir H. Patel, Nathan Y. Yu, Chris L. Hallemeier, Brooke E. Ball, Mark R. Waddle, Wei Liu
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 RadOnc-GPT is a specialized Large Language Model designed to assist in generating responses to patients’ inquiries in the context of radiotherapeutic treatment of prostate cancer. The model was integrated with patient electronic health records from both hospital-wide and radiation-oncology-specific databases, and evaluated on 158 previously recorded in-basket message interactions. RadOnc-GPT slightly outperformed clinical care teams in terms of “Clarity” and “Empathy”, while achieving comparable scores in “Completeness” and “Correctness”. The model has the potential to reduce healthcare costs by producing high-quality, timely responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RadOnc-GPT is a computer program that helps doctors and nurses respond quickly and correctly to patients’ questions. It’s designed specifically for prostate cancer treatment and uses patient information from medical records. The program was tested on many examples of patient inquiries and did well in understanding the messages and responding in a way that’s clear and kind. Using RadOnc-GPT could help reduce the time doctors and nurses spend answering patient questions, which might lead to cost savings. |
Keywords
» Artificial intelligence » Gpt » Large language model
