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This paper is in the following e-collection/theme issue:

Pilot studies (ehealth) (687) Digital Health Reporting Standards, Quality and Transparency in e-Research (245) Formative Evaluation of Digital Health Interventions (5351) Research Methodology - AI (202) Generative Language Models Including ChatGPT (1620) Applications of AI (1075) Responsible Health AI (254) Tools and Software for Metascience and Research Integrity Research (26)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68666, first published .
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Evaluating a Customized Version of ChatGPT for Systematic Review Data Extraction in Health Research: Development and Usability Study

Evaluating a Customized Version of ChatGPT for Systematic Review Data Extraction in Health Research: Development and Usability Study

Authors of this article:

Jayden Sercombe1 Author Orcid Image ;   Zachary Bryant1 Author Orcid Image ;   Jack Wilson1 Author Orcid Image
Article Authors Cited by (15) Tweetations Metrics

Journals

  1. Goel P, Weber R. Measuring Interpretability: A Systematic Literature Review of Interpretability Measures in Artificial Intelligence. International Journal on Artificial Intelligence Tools 2026;35(02) View
  2. Liu J, Ding X, Gan Y. AI-powered methods for psychological assessment in adolescence psychological disorders: A systematic review and meta-analysis. Clinical Psychology Review 2026;126:102728 View
  3. Mortezaagha P, Shaw J, Sun B, Rahgozar A. From chaos to clarity: schema-constrained AI for auditable biomedical evidence extraction from full-text PDFs. BMC Medical Research Methodology 2026;26(1) View
  4. Vivekanantha P, Kahlon H, Balogun O, Bouchard M, de Sa D, Ayeni O, Kay J. Automated data extraction for systematic reviews using GPT‐5.2 and Google Gemini Pro 3: A dual‐large language model approach in orthopaedic research. Knee Surgery, Sports Traumatology, Arthroscopy 2026;34(8):3011 View
  5. Oami T, Okada Y, Maeda K, Nakada T. Performance of large language models and prompt engineering strategies for data extraction in systematic reviews. Frontiers in Digital Health 2026;8 View
  6. Bernal-Córdoba C, Valldecabres A, Borona-Valencia M, Canales-Muñoz O, Silva-del-Río N. A rule-based large language model screening framework for evidence synthesis in animal health: Feasibility evaluation in a veterinary scoping review. Preventive Veterinary Medicine 2026;254:106908 View
  7. Zhang W, Nguyen T, Stuart E, Chen Y. Large language models for full-text methods assessment: a case study on mediation analysis. Journal of the American Medical Informatics Association 2026;33(9):1702 View
  8. Hollinger-Smith L. Plotting the Course for Artificial Intelligence in Nursing Education Research Methodologies (Part 1). Nursing Education Perspectives 2026;47(4):258 View
  9. Munguía-Realpozo P, Ramírez-Lara E, Mendoza-Pinto C, Etchegaray-Morales I, Solis-Poblano J, Trinidad-González M, Ayón-Aguilar J, García-Flores M, Montiel-Jarquín Á. Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal. DIGITAL HEALTH 2026;12 View
  10. Lai H, Li Y. Beyond accuracy: assessing the information quality of large language models for systematic literature reviews. Telematics and Informatics 2026;109:102447 View
  11. Shankar R, Lim A, Qian X. Performance of large language models in data extraction for evidence synthesis: A systematic review. Journal of Biomedical Informatics 2026;181:105086 View
  12. Castillo Badilla C. Modelo replicable de ingeniería de prompts para la recuperación de literatura científica con ChatGPT. Encontros Bibli: revista eletrônica de biblioteconomia e ciência da informação 2026;31:1 View
  13. Parkington K, Joseph J, Musleh K, Rouleau-Tang M, Persaud A, Rueda A, Teferra B, Al-Shamali H, Mao W, Nezhad F, Burback L, Zhang Y, Jetly R, Vermetten E, Monson C, Krishnan S, Zeifman R, Greenshaw A, Lou W, Bhat V, Watson M. Leveraging prompt-driven generative AI for systematic reviews in digital psychiatry: A stage-matched comparative proof-of-concept for healthcare researchers and clinicians. PLOS Digital Health 2026;5(9):e0001666 View
  14. Sato Y, Tanaka K. AI-Assisted Systematic Evidence Mapping Study Reveals An Unexplored Translational Paradigm: Intranasal Delivery of Adipose-Derived Mesenchymal Stem Cells for Parkinson’s Disease. SN Comprehensive Clinical Medicine 2026;8(1) View
  15. Adam G, Davies M, Caputo E, Mai H, Coppola E, Holmer H, Kuhn E, Balk E, Trikalinos T, Ivlev I, Wethington H. Artificial intelligence tools for evidence synthesis in the biomedical field: A living rapid review and evidence map. Research Synthesis Methods 2026:1 View