{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:08:04Z","timestamp":1782846484979,"version":"3.54.5"},"reference-count":32,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Educ"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The integration of artificial intelligence (AI) and machine learning (ML) into biomedical research requires a workforce fluent in both computational methods and clinical applications. Structured, interdisciplinary training opportunities remain limited, creating a gap between data scientists and clinicians. The National Institutes of Health\u2019s Bridge to Artificial Intelligence (Bridge2AI) initiative launched the Artificial Intelligence\u2013Ready and Exploratory Atlas for Diabetes Insights (AI-READI) data generation project to address this gap. AI-READI is creating a multimodal, FAIR (findable, accessible, interoperable, and reusable) dataset\u2014including ophthalmic imaging, physiologic measurements, wearable sensor data, and survey responses\u2014from approximately 4000 participants with or at risk for type 2 diabetes. In parallel, AI-READI established a year-long mentored research program that begins with a 2-week immersive summer bootcamp to provide foundational AI\/ML skills grounded in domain-relevant biomedical data.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To describe the design, iterative refinement, and outcomes of the AI-READI Bootcamp, and to share lessons for creating future multidisciplinary AI\/ML training programs in biomedical research.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Held annually at the University of California San Diego, the bootcamp combines 80 hours of lectures, coding sessions, and small-group mentorship. Year 1 introduced Python programming, classical ML techniques (eg, logistic regression, convolutional neural networks), and data science methods, such as principal component analysis and clustering, using public datasets. In Year 2, the curriculum was refined based on structured participant feedback\u2014reducing cohort size to increase individualized mentorship, integrating the AI-READI dataset (including retinal images and structured clinical variables), and adding modules on large language models and FAIR data principles. Participant characteristics and satisfaction were assessed through standardized pre- and postbootcamp surveys, and qualitative feedback was analyzed thematically by independent coders.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Seventeen participants attended Year 1 and 7 attended Year 2, with an instructor-to-student ratio of approximately 1:2 in the latter. Across both years, postbootcamp evaluations indicated high satisfaction, with Year 2 participants reporting improved experiences due to smaller cohorts, earlier integration of the AI-READI dataset, and greater emphasis on applied learning. In Year 2, mean scores for instructor effectiveness, staff support, and overall enjoyment were perfect (5.00\/5.00). Qualitative feedback emphasized the value of working with domain-relevant, multimodal datasets; the benefits of peer collaboration; and the applicability of skills to structured research projects during the subsequent internship year.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The AI-READI Bootcamp illustrates how feedback-driven, multidisciplinary training embedded within a longitudinal mentored research program can bridge technical and clinical expertise in biomedical AI. Core elements\u2014diverse trainee cohorts, applied learning with biomedical datasets, and sustained mentorship\u2014offer a replicable model for preparing health professionals for the evolving AI\/ML landscape. Future iterations will incorporate additional prebootcamp onboarding modules, objective skill assessments, and long-term tracking of research engagement and productivity.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/83154","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T02:55:09Z","timestamp":1762743309000},"page":"e83154-e83154","source":"Crossref","is-referenced-by-count":2,"title":["Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based on the AI-READI Bootcamp"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1081-6180","authenticated-orcid":false,"given":"Taiki W","family":"Nishihara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3940-2272","authenticated-orcid":false,"given":"Fritz Gerald P","family":"Kalaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1015-2712","authenticated-orcid":false,"given":"Adelle","family":"Engmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3004-1627","authenticated-orcid":false,"given":"Aya","family":"Motoyoshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9471-2256","authenticated-orcid":false,"given":"Paapa","family":"Mensah-Kane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7263-0891","authenticated-orcid":false,"given":"Deepa","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3504-6514","authenticated-orcid":false,"given":"Victoria","family":"Patronilo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1143-5224","authenticated-orcid":false,"given":"Linda M","family":"Zangwill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9541-1915","authenticated-orcid":false,"given":"Shahin","family":"Hallaj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4624-1219","authenticated-orcid":false,"given":"Amirhossein","family":"Panahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7538-1715","authenticated-orcid":false,"given":"Garrison W","family":"Cottrell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1640-2525","authenticated-orcid":false,"given":"Bradley","family":"Voytek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0989-3576","authenticated-orcid":false,"given":"Virginia R","family":"de Sa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-7690","authenticated-orcid":false,"given":"Sally L","family":"Baxter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"issue":"11","key":"R1","doi-asserted-by":"publisher","DOI":"10.2196\/14452","article-title":"Development of a deep learning model for dynamic forecasting of blood glucose level for type 2 diabetes mellitus: secondary analysis of a randomized controlled trial","volume":"7","author":"Faruqui","journal-title":"JMIR Mhealth Uhealth"},{"issue":"3","key":"R2","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1093\/jamia\/ocaa296","article-title":"Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review","volume":"28","author":"Schwartz","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-84698-5","article-title":"A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology","volume":"11","author":"Scheetz","journal-title":"Sci Rep"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-024-03566-x","article-title":"Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions","volume":"22","author":"Chen","journal-title":"BMC Med"},{"issue":"7","key":"R5","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.10.7.14","article-title":"Impact of artificial intelligence on medical education in ophthalmology","volume":"10","author":"Valikodath","journal-title":"Transl Vis Sci Technol"},{"issue":"14","key":"R6","doi-asserted-by":"publisher","first-page":"e1028","DOI":"10.1161\/CIR.0000000000001201","article-title":"Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association","volume":"149","author":"Armoundas","journal-title":"Circulation"},{"issue":"12","key":"R7","doi-asserted-by":"publisher","first-page":"2210","DOI":"10.1038\/s42255-024-01165-x","article-title":"AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond","volume":"6","author":"Baxter","journal-title":"Nat Metab"},{"key":"R8","unstructured":"AI-READI Consortium. Flagship dataset of type 2 diabetes from the AI-READI project. FAIRhub. 2024. [Accessed 03-12-2025]"},{"key":"R9","unstructured":"Bridge to Artificial Intelligence (Bridge2AI). NIH Common Fund. URL: https:\/\/commonfund.nih.gov\/bridge2ai [Accessed 08-08-2025]"},{"issue":"4","key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/31043","article-title":"Artificial intelligence education programs for health care professionals: scoping review","volume":"7","author":"Charow","journal-title":"JMIR Med Educ"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.2196\/54793","article-title":"Curriculum frameworks and educational programs in AI for medical students, residents, and practicing physicians: scoping review","volume":"10","author":"Tolentino","journal-title":"JMIR Med Educ"},{"issue":"3","key":"R12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.30476\/JAMP.2023.98655.1803","article-title":"Application of artificial intelligence in medical education: current scenario and future perspectives","volume":"11","author":"Mir","journal-title":"J Adv Med Educ Prof"},{"key":"R13","doi-asserted-by":"crossref","unstructured":"National Academies of Sciences, Engineering, and Medicine. Artificial Intelligence in Health Professions Education: Proceedings of a Workshop. The National Academies Press; 2023. [doi: 10.17226\/27174]","DOI":"10.17226\/27174"},{"key":"R14","unstructured":"eCFR :: 45 CFR Part 46 -- Protection of Human Subjects. Electronic Code of Federal Regulations (eCFR). URL: https:\/\/www.ecfr.gov\/current\/title-45\/subtitle-A\/subchapter-A\/part-46 [Accessed 19-12-2025]"},{"key":"R15","unstructured":"Orgill BD Nolin J . Learning taxonomies in medical simulation. In: StatPearls. StatPearls Publishing; 2025. URL: http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK559109\/ [Accessed 03-12-2025]"},{"issue":"26","key":"R16","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1056\/NEJMp1702071","article-title":"Machine learning and prediction in medicine - beyond the peak of inflated expectations","volume":"376","author":"Chen","journal-title":"N Engl J Med"},{"issue":"8","key":"R17","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1097\/ACM.0000000000002044","article-title":"Medical education must move from the information age to the age of artificial intelligence","volume":"93","author":"Wartman","journal-title":"Acad Med"},{"issue":"5","key":"R18","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1080\/23808993.2017.1380516","article-title":"The role of artificial intelligence in precision medicine","volume":"2","author":"Mesko","journal-title":"Expert Rev Precis Med Drug Dev"},{"issue":"3-4","key":"R19","first-page":"303","volume":"25","author":"Haidet","journal-title":"J Excell Coll Teach"},{"issue":"2","key":"R20","doi-asserted-by":"publisher","DOI":"10.1187\/cbe.18-02-0023","article-title":"Getting messy with authentic data: exploring the potential of using data from scientific research to support student data literacy","volume":"18","author":"Kjelvik","journal-title":"CBE Life Sci Educ"},{"key":"R21","unstructured":"AIM-AHEAD programs. AIM-AHEAD. URL: https:\/\/www.aim-ahead.net\/programs\/ [Accessed 03-12-2025]"},{"key":"R22","unstructured":"Dive into Deep Learning. URL: https:\/\/d2l.ai\/ [Accessed 03-12-2025]"},{"key":"R23","unstructured":"AI-READI bootcamp. GitHub. URL: https:\/\/github.com\/voytek\/AI-READI-Bootcamp [Accessed 03-12-2025]"},{"issue":"1","key":"R24","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00521-5","article-title":"Digital medicine and the curse of dimensionality","volume":"4","author":"Berisha","journal-title":"NPJ Digit Med"},{"key":"R25","unstructured":"Bishop C . Pattern Recognition and Machine Learning. Springer; 2006."},{"key":"R26","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.43979","article-title":"Data science for the scientific life cycle","volume":"8","author":"Ezer","journal-title":"Elife"},{"issue":"6464","key":"R27","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","journal-title":"Science"},{"key":"R28","doi-asserted-by":"crossref","unstructured":"Rumelhart H Williams C . Learning internal representations by error propagation. In: Parallel Distributed Processing, Volume 1: Explorations in the Microstructure of Cognition: Foundations. 1987:318-362. URL: https:\/\/dl.acm.org\/doi\/10.5555\/104279.104293 [Accessed 03-12-2025]","DOI":"10.7551\/mitpress\/4943.003.0128"},{"key":"R29","unstructured":"Strang C . Eigenvalues and eigenvectors. In: Introduction to Linear Algebra. 5th ed. Wellesley-Cambridge Press; 2016."},{"key":"R30","unstructured":"Lec 1 | MIT 18.06 linear algebra, spring 2005. MIT OpenCourseWare YouTube page. May 6, 2009. URL: https:\/\/www.youtube.com\/watch?v=ZK3O402wf1c&list=PL49CF3715CB9EF31D&index=1 [Accessed 17-12-2025]"},{"key":"R31","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for scientific data management and stewardship","volume":"3","author":"Wilkinson","journal-title":"Sci Data"},{"issue":"7714","key":"R32","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1038\/d41586-018-05707-8","article-title":"AI can be sexist and racist - it\u2019s time to make it fair","volume":"559","author":"Zou","journal-title":"Nature New Biol"}],"container-title":["JMIR Medical Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/mededu.jmir.org\/2025\/1\/e83154","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:17:14Z","timestamp":1782843434000},"score":1,"resource":{"primary":{"URL":"https:\/\/mededu.jmir.org\/2025\/1\/e83154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":32,"URL":"https:\/\/doi.org\/10.2196\/83154","relation":{},"ISSN":["2369-3762"],"issn-type":[{"value":"2369-3762","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"article-number":"v11i11e83154"}}