Apr 24 2024Mass General Brigham A new study by investigators from Mass General Brigham demonstrates that large language models , a type of generative AI, may help reduce physician workload and improve patient education when used to draft replies to patient messages. The study also found limitations to LLMs that may affect patient safety, suggesting that vigilant oversight of LLM-generated communications is essential for safe usage.
Generative AI has the potential to provide a 'best of both worlds' scenario of reducing burden on the clinician and better educating the patient in the process. However, based on our team's experience working with LLMs, we have concerns about the potential risks associated with integrating LLMs into messaging systems. With LLM-integration into EHRs becoming increasingly common, our goal in this study was to identify relevant benefits and shortcomings.
For the study, the researchers used OpenAI's GPT-4, a foundational LLM, to generate 100 scenarios about patients with cancer and an accompanying patient question. No questions from actual patients were used for the study. Six radiation oncologists manually responded to the queries; then, GPT-4 generated responses to the questions. Finally, the same radiation oncologists were provided with the LLM-generated responses for review and editing.
Related StoriesNotably, LLM-generated/physician-edited responses were more similar in length and content to LLM-generated responses versus the manual responses. In many cases, physicians retained LLM-generated educational content, suggesting that they perceived it to be valuable. While this may promote patient education, the researchers emphasize that overreliance on LLMs may also pose risks, given their demonstrated shortcomings.
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