Evidence map›Paper›PMID 37990659›Full record

ArticleObesity pillars2023

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023.

Harold Edward Bays, Angela Fitch, Suzanne Cuda, Sylvia Gonsahn-Bollie, Elario Rickey, Joan Hablutzel, Rachel Coy, Marisa Censani

Open access · diamondAbstract read
In one paragraph

Article in Obesity pillars, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
3.8field-weighted citation impact, top 5% of its field
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

41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 100 citations in OpenAlex.

  1. A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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  10. Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025
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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

8 authors at 3 institutions in 1 country.

Harold Edward BaysLouisville Metabolic and Atherosclerosis Research Center, University of Louisville School of Medicine, 3288 Illinois Avenue, Louisville, KY, 40213, USA.
Angela Fitch15 Oak Street Suite 3 Needham, MA, 02492, USA.
Suzanne CudaAlamo City Healthy Kids and Families, 1919 Oakwell Farms Parkway Ste 145, San Antonio, TX, 78218, USA.
Sylvia Gonsahn-BollieEmbrace You Weight & Wellness, 8705 Colesville Rd Suite 103, Silver Spring, MD, 10, USA.
Elario RickeyObesity Medicine Association, 7173 S. Havana St. #600-130, Centennial, CO, 80112, USA.
Joan HablutzelObesity Medicine Association, 7173 S. Havana St. #600-130, Centennial, CO, 80112, USA.
Rachel CoyObesity Medicine Association, 7173 S. Havana St. #600-130, Centennial, CO, 80112, USA.
Marisa CensaniDivision of Pediatric Endocrinology, Department of Pediatrics, New York Presbyterian Hospital, Weill Cornell Medicine, 525 East 68th Street, Box 103, New York, NY, 10021, USA.
Obesity Medicine Association · USNewYork–Presbyterian Hospital · USUniversity of Louisville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity. Methods: The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT). Results: Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records). Conclusions: AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.

Indexed as

AdiposopathyArtificial intelligenceEducationObesity

Identifiers

PMID37990659
PMCPMC10662105
OpenAlexW4366498123

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.