<b>Assessment of Large Language Models for Enhancing Diabetologist-Developed Personalized Treatment Plans in Complex Type 2 Diabetes</b>
<p dir="ltr">Treatment decisions for type 2 diabetes are complex and multifactorial. This study evaluated the utility of OpenAI’s generative pre-trained transformer-4 (GPT-4) large language model in enhancing treatment plans for people with diabetes. The authors assessed GPT-4’s ability to extract and critically evaluate diabetologist-developed treatment plans from patients’ clinical notes. GPT-4 accurately discerned personalized treatment goals but performed suboptimally when identifying plans that could be further improved and personalized. However, 40% of GPT-4’s reasoning and explanations were still considered helpful by experienced academic diabetologists, suggesting a potential broad benefit to less expert physicians, such as those in primary care settings.</p><p><br></p><p dir="ltr"><b>Key Points</b></p><p dir="ltr">· GPT-4 has demonstrated potential in retaining complex concepts crucial for developing type 2 diabetes personalized treatment plans. This study evaluated the utility of GPT-4 in enhancing treatment plans for actual patients with diabetes.</p><p dir="ltr">· GPT-4 accurately discerned personalized treatment goals but was suboptimal in identifying diabetologist-developed treatment plans that could be further improved and personalized. Even so, 40% of GPT-4’s reasoning and explanations were considered helpful when evaluated by blinded expert academic diabetologists.</p><p dir="ltr">· These findings suggest the potential of broad benefit of GPT-4 in enhancing treatment plans to less expert physicians, such as those practicing in primary care settings.</p>
Authors
- Madhumita Sushil (ORCID: https://orcid.org/0000-0001-7884-0526)
- Victoria Hsiao (ORCID: https://orcid.org/0000-0001-9297-1522)
- Brenda Y. Miao (ORCID: https://orcid.org/0000-0002-3393-9837)
- Atul J. Butte (ORCID: https://orcid.org/0000-0002-7433-2740)
- Michelle Wang (ORCID: https://orcid.org/0000-0002-3207-1490)
- Christopher Y. K. Williams (ORCID: https://orcid.org/0000-0001-8867-1623)
- Umesh Masharani (ORCID: https://orcid.org/0000-0002-3269-804X)
- Gregory Ku (ORCID: https://orcid.org/0000-0001-5306-4225)
- Aloukika Shah
- Suneil K. Koliwad (ORCID: https://orcid.org/0000-0002-7367-1054)
- Sarah Kim (ORCID: https://orcid.org/0000-0002-3599-5509)
Publication Details
- Published
- 2025-08-20
- DOI
- https://doi.org/10.2337/figshare.29464577
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00