An Artificial Intelligence-Assisted Umbrella Review of Eating Disorder Meta-Analyses (2020–2025): Mapping Treatment Efficacy, Comorbidity, and Persistent Gaps in Sample Diversity
6 affiliations
- 1Department of Psychiatry, Columbia University, New York City, NY 10032, USA
- 2New York State Psychiatric Institute, New York City, NY 10032, USA
- 3Department of Psychiatry, Universidad Complutense, 28040 Madrid, Spain
- 4Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23284, USA
- 5Williams College, Williamstown, MA 01267, USA
- 6Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA 94143, USA
Abstract
Background/Objectives: Eating disorders (EDs) are serious psychiatric illnesses that result in physical and mental-health impairments. A unifying summary of the broad collection of ED research findings is lacking. This umbrella review leveraged artificial intelligence (AI) to summarize ED-focused meta-analyses, to identify directions for future research and treatment. Methods: PubMed, PsycINFO, Web of Science, Embase, and ProQuest were searched for meta-analyses of human ED research published between 1 June 2020 and 31 May 2025 (inclusive). A large language model (LLM) conducted risk of bias assessment and data extraction for studies meeting all inclusion criteria. Neural topic modeling (BERTopic) identified the themes present across included meta-analyses, and an LLM summarized findings of articles in each category. Results: 220 meta-analyses were included. AI tools demonstrated good accuracy in quality appraisal, data extraction, and data synthesis (97%, 93%, and 96%, respectively). Most meta-analyses had at least one critical flaw (per AMSTAR 2 quality appraisal). Neural topic modeling identified 16 themes within the set of included articles: Neurocognition, Treatment Effectiveness, Intervention Efficacy, Biomarkers, Premorbid Conditions, Clinical Features, Epidemiology, Psychiatric Comorbidity, Behavioral Addictions, Long-Term Outcomes, Prevention, Psychological Factors, Treatment Gaps, COVID-19, Pharmacotherapy, and Avoidant Restrictive Food Intake Disorder. Article summaries collectively identified ED prevalence of 1.69% and associations of EDs with mortality and disability. Studies covered a range of illness mechanisms and treatments, though disproportionately included Western females. Conclusions: This review highlights the broad scope but limited generalizability of ED research, as well as the potential of AI to accelerate systematic review workflows.
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BibTeX
@article{lloyd2026artificial,
title = {{An Artificial Intelligence-Assisted Umbrella Review of Eating Disorder Meta-Analyses (2020–2025): Mapping Treatment Efficacy, Comorbidity, and Persistent Gaps in Sample Diversity}},
author = {E. Caitlin Lloyd and Pablo Del Sol Calderon and Cate Morales and Euna Lee and Sarita Maharajh and Brooke Moorman and Daniel Le Grange and Kelsey Hagan and Sasha Gorrell},
journal = {Nutrients},
year = {2026},
month = nov,
doi = {10.3390/nu18213650},
url = {https://www.mdpi.com/2072-6643/18/21/3650},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An Artificial Intelligence-Assisted Umbrella Review of Eating Disorder Meta-Analyses (2020–2025): Mapping Treatment Efficacy, Comorbidity, and Persistent Gaps in Sample Diversity
AU - E. Caitlin Lloyd
AU - Pablo Del Sol Calderon
AU - Cate Morales
AU - Euna Lee
AU - Sarita Maharajh
AU - Brooke Moorman
AU - Daniel Le Grange
AU - Kelsey Hagan
AU - Sasha Gorrell
PY - 2026
DA - 2026/11/03
JO - Nutrients
DO - 10.3390/nu18213650
UR - https://www.mdpi.com/2072-6643/18/21/3650
AB - Background/Objectives: Eating disorders (EDs) are serious psychiatric illnesses that result in physical and mental-health impairments. A unifying summary of the broad collection of ED research findings is lacking. This umbrella review leveraged artificial intelligence (AI) to summarize ED-focused meta-analyses, to identify directions for future research and treatment. Methods: PubMed, PsycINFO, Web of Science, Embase, and ProQuest were searched for meta-analyses of human ED research published between 1 June 2020 and 31 May 2025 (inclusive). A large language model (LLM) conducted risk of bias assessment and data extraction for studies meeting all inclusion criteria. Neural topic modeling (BERTopic) identified the themes present across included meta-analyses, and an LLM summarized findings of articles in each category. Results: 220 meta-analyses were included. AI tools demonstrated good accuracy in quality appraisal, data extraction, and data synthesis (97%, 93%, and 96%, respectively). Most meta-analyses had at least one critical flaw (per AMSTAR 2 quality appraisal). Neural topic modeling identified 16 themes within the set of included articles: Neurocognition, Treatment Effectiveness, Intervention Efficacy, Biomarkers, Premorbid Conditions, Clinical Features, Epidemiology, Psychiatric Comorbidity, Behavioral Addictions, Long-Term Outcomes, Prevention, Psychological Factors, Treatment Gaps, COVID-19, Pharmacotherapy, and Avoidant Restrictive Food Intake Disorder. Article summaries collectively identified ED prevalence of 1.69% and associations of EDs with mortality and disability. Studies covered a range of illness mechanisms and treatments, though disproportionately included Western females. Conclusions: This review highlights the broad scope but limited generalizability of ED research, as well as the potential of AI to accelerate systematic review workflows.
SN - 2072-6643
ER -