Artificial Intelligence and Obesity Management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Overview

Journal Obes Pillars

Date 2023 Nov 22

PMID 37990659

Authors

Harold Edward Bays

Angela Fitch

Suzanne Cuda

Sylvia Gonsahn-Bollie

Elario Rickey

Joan Hablutzel

Rachel Coy

Marisa Censani

Affiliations

Soon will be listed here.

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.

Citing Articles

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References

Azer S, AlSwaidan N, Alshwairikh L, AlShammari J . Accuracy and readability of cardiovascular entries on Wikipedia: are they reliable learning resources for medical students?. BMJ Open. 2015; 5(10):e008187. PMC: 4606442. DOI: 10.1136/bmjopen-2015-008187. View

Phelan S, Burgess D, Yeazel M, Hellerstedt W, Griffin J, van Ryn M . Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes Rev. 2015; 16(4):319-26. PMC: 4381543. DOI: 10.1111/obr.12266. View

Pennings N, Golden L, Yashi K, Tondt J, Bays H . Sleep-disordered breathing, sleep apnea, and other obesity-related sleep disorders: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022. Obes Pillars. 2023; 4:100043. PMC: 10662058. DOI: 10.1016/j.obpill.2022.100043. View

DeGregory K, Kuiper P, DeSilvio T, Pleuss J, Miller R, Roginski J . A review of machine learning in obesity. Obes Rev. 2018; 19(5):668-685. PMC: 8176949. DOI: 10.1111/obr.12667. View

Freshwater M, Christensen S, Oshman L, Bays H . Behavior, motivational interviewing, eating disorders, and obesity management technologies: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022. Obes Pillars. 2023; 2:100014. PMC: 10661888. DOI: 10.1016/j.obpill.2022.100014. View

Wang B, Torriani M . Artificial Intelligence in the Evaluation of Body Composition. Semin Musculoskelet Radiol. 2020; 24(1):30-37. DOI: 10.1055/s-0039-3400267. View

Flanagin A, Bibbins-Domingo K, Berkwits M, Christiansen S . Nonhuman "Authors" and Implications for the Integrity of Scientific Publication and Medical Knowledge. JAMA. 2023; 329(8):637-639. DOI: 10.1001/jama.2023.1344. View

Zarkogianni K, Chatzidaki E, Polychronaki N, Kalafatis E, Nicolaides N, Voutetakis A . The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity. Nutrients. 2023; 15(6). PMC: 10057317. DOI: 10.3390/nu15061451. View

Karjoo S, Auriemma A, Fraker T, Bays H . Nonalcoholic fatty liver disease and obesity: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022. Obes Pillars. 2023; 3:100027. PMC: 10661876. DOI: 10.1016/j.obpill.2022.100027. View

10.

Fitch A, Bays H . Obesity definition, diagnosis, bias, standard operating procedures (SOPs), and telehealth: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022. Obes Pillars. 2023; 1:100004. PMC: 10661988. DOI: 10.1016/j.obpill.2021.100004. View

11.

LEUCUTIA T . Heuristic glimpses into the evolution of radiation therapy; particularly in reference to gynecologic cancers. Am J Roentgenol Radium Ther Nucl Med. 1961; 85:3-20. View

12.

Chatelan A, Bochud M, Frohlich K . Precision nutrition: hype or hope for public health interventions to reduce obesity?. Int J Epidemiol. 2018; 48(2):332-342. PMC: 6469305. DOI: 10.1093/ije/dyy274. View

13.

Han K, Cao P, Wang Y, Xie F, Ma J, Yu M . A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning. Front Pharmacol. 2022; 12:814858. PMC: 8835726. DOI: 10.3389/fphar.2021.814858. View

14.

Harskamp R, De Clercq L . Performance of ChatGPT as an AI-assisted decision support tool in medicine: a proof-of-concept study for interpreting symptoms and management of common cardiac conditions (AMSTELHEART-2). Acta Cardiol. 2024; 79(3):358-366. DOI: 10.1080/00015385.2024.2303528. View

15.

Disse E, Ledoux S, Betry C, Caussy C, Maitrepierre C, Coupaye M . An artificial neural network to predict resting energy expenditure in obesity. Clin Nutr. 2017; 37(5):1661-1669. DOI: 10.1016/j.clnu.2017.07.017. View

16.

Rahmanti A, Yang H, Bintoro B, Nursetyo A, Muhtar M, Syed-Abdul S . SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding. Front Nutr. 2022; 9:870775. PMC: 9260382. DOI: 10.3389/fnut.2022.870775. View

17.

McCormick D, Bhatt D, Bays H, Taub P, Caldwell K, Guerin C . A regional analysis of payer and provider views on cholesterol management: PCSK9 inhibitors as an illustrative alignment model. J Manag Care Spec Pharm. 2020; 26(12):1517-1528. PMC: 10391214. DOI: 10.18553/jmcp.2020.26.12.1517. View

18.

Huang J, Galal G, Etemadi M, Vaidyanathan M . Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review. JMIR Med Inform. 2022; 10(5):e36388. PMC: 9198828. DOI: 10.2196/36388. View

19.

Nam D, Chapiro J, Paradis V, Seraphin T, Kather J . Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep. 2022; 4(4):100443. PMC: 8867112. DOI: 10.1016/j.jhepr.2022.100443. View

20.

Oh W, An Y, Min S, Park C . Comparative Effectiveness of Artificial Intelligence-Based Interactive Home Exercise Applications in Adolescents with Obesity. Sensors (Basel). 2022; 22(19). PMC: 9573710. DOI: 10.3390/s22197352. View