Evidence map›Paper›PMID 41140742›Full record

ArticleClinical diabetes : a publication of the American Diabetes Association2025

Assessment of Large Language Models for Enhancing Diabetologist-Developed Personalized Treatment Plans in Complex Type 2 Diabetes.

Michelle Wang, Madhumita Sushil, Christopher Y K Williams, Brenda Y Miao, Sarah Kim, Umesh Masharani, Gregory Ku, Victoria Hsiao, Aloukika Shah, Suneil K Koliwad and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical diabetes : a publication of the American Diabetes Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Michelle WangDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA.ORCID https://orcid.org/0000-0002-3207-1490
Madhumita SushilBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Christopher Y K WilliamsBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Brenda Y MiaoBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Sarah KimDiabetes Center, University of California, San Francisco, San Francisco, CA.
Umesh MasharaniDiabetes Center, University of California, San Francisco, San Francisco, CA.ORCID https://orcid.org/0000-0002-3269-804X
Gregory KuDiabetes Center, University of California, San Francisco, San Francisco, CA.ORCID https://orcid.org/0000-0001-5306-4225
Victoria HsiaoDiabetes Center, University of California, San Francisco, San Francisco, CA.
Aloukika ShahSchool of Medicine, University of California, San Francisco, San Francisco, CA.
Suneil K KoliwadDiabetes Center, University of California, San Francisco, San Francisco, CA.ORCID https://orcid.org/0000-0002-7367-1054
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID https://orcid.org/0000-0002-7433-2740

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
CLINICAL, PHARMACOLOGY, DRUG ACTION &PHARMACOKINETICST32GM007546 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Radojka Savic · 1985 to 2026
$8.9M
NCATS NIH HHS UL1 TR001872NIGMS NIH HHS T32 GM007546
6 · The paper itself

Abstract

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.

Identifiers

PMID41140742
PMCPMC12547058

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.