Evidence map›Paper›PMID 39622675›Full record

ArticleJMIR diabetes2024

Lightening the Load: Generative AI to Mitigate the Burden of the New Era of Obesity Medical Therapy.

Elizabeth R Stevens, Arielle Elmaleh-Sachs, Holly Lofton, Devin M Mann

Abstract read
In one paragraph

Article in JMIR diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
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

4 authors.

Elizabeth R StevensDepartment of Population Health, New York University Grossman School of Medicine, 227 e 30th st., New York, NY, 10016, United States, 1 646-501-2558.ORCID http://orcid.org/0000-0001-6063-1523
Arielle Elmaleh-SachsDepartment of Population Health, New York University Grossman School of Medicine, 227 e 30th st., New York, NY, 10016, United States, 1 646-501-2558.ORCID http://orcid.org/0009-0004-0666-1501
Holly LoftonDepartment of Medicine, New York University Grossman School of Medicine, New York, NY, United States.ORCID http://orcid.org/0000-0002-3403-882X
Devin M MannDepartment of Population Health, New York University Grossman School of Medicine, 227 e 30th st., New York, NY, 10016, United States, 1 646-501-2558.ORCID http://orcid.org/0000-0002-2099-0852

Funding

New York Regional Center for Diabetes Translation Research - Translational Intervention Methodology CoreP30DK111022 · NIDDK · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI JEFFREY GONZALEZ · 2016 to 2026
$7.8M
AIManage using an AI-driven CDS and chatbot for the management of patients on incretin mimetic medicationsR01DK144008 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Elizabeth Reid Stevens · 2025 to 2026
$1.7M
NIDDK NIH HHS P30 DK111022NIDDK NIH HHS R01 DK144008
6 · The paper itself

Abstract

Unlabelled: Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems.

Indexed as

agonistsAIantiobesityartificial intelligenceclinical managementcliniciandiabetesGLP-1glucagon-like peptide 1glucose-dependent insulinotropic polypeptidehealth care delivery systemincretin mimeticmedical therapymedicationobesitytreatment

Identifiers

PMID39622675
PMCPMC11611792

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.