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

Viewpoint (41) Machine Learning (1745) Decision Support for Health Professionals (1344)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33970, first published .
Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges

Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges

Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges

Authors of this article:

Keerthi B Harish1 Author Orcid Image ;   W Nicholson Price2, 3 Author Orcid Image ;   Yindalon Aphinyanaphongs1 Author Orcid Image
Article Authors Cited by (13) Tweetations (3) Metrics

Journals

  1. Sarkis R, Burri O, Royer-Chardon C, Schyrr F, Blum S, Costanza M, Cherix S, Piazzon N, Barcena C, Bisig B, Nardi V, Sarro R, Ambrosini G, Weigert M, Spertini O, Blum S, Deplancke B, Seitz A, de Leval L, Naveiras O. MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology. Modern Pathology 2023;36(4):100088 View
  2. Taribagil P, Hogg H, Balaskas K, Keane P. Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities. Expert Review of Ophthalmology 2023;18(1):45 View
  3. Jiang D, Chen Z, Ma F, Gong Y, Pu T, Chen J, Liu X, Zhao Y, Xie K, Hou H, Wang C, Geng X, Liu F. Online calculator for predicting the risk of malignancy in patients with pancreatic cystic neoplasms: A multicenter, retrospective study. World Journal of Gastroenterology 2022;28(37):5469 View
  4. Hu W, Yii F, Chen R, Zhang X, Shang X, Kiburg K, Woods E, Vingrys A, Zhang L, Zhu Z, He M. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Translational Vision Science & Technology 2023;12(7):14 View
  5. Chickering M, Frank E, Caplan A. Standing on the Shoulders of Giant Artificial Intelligence Bots: Artificial Intelligence Can and Therefore Must Now Elevate Equity in Health Professional Education. AJPM Focus 2024;3(1):100168 View
  6. Lee Y, Stretton B, Tan S, Gupta A, Kovoor J, Bacchi S, Lim W, Chan W. Captive markets and medical artificial intelligence. Journal of Medical Imaging and Radiation Oncology 2024;68(3):278 View
  7. Petrella R. The AI Future of Emergency Medicine. Annals of Emergency Medicine 2024;84(2):139 View
  8. Oikonomou E, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. European Heart Journal 2024;45(35):3204 View
  9. Link K, Schnurman Z, Liu C, Kwon Y, Jiang L, Nasir-Moin M, Neifert S, Alzate J, Bernstein K, Qu T, Chen V, Yang E, Golfinos J, Orringer D, Kondziolka D, Oermann E. Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark. Nature Communications 2024;15(1) View
  10. Woo J, Yang A, Olsen R, Hasan S, Nawabi D, Nwachukwu B, Williams R, Ramkumar P. Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine. Arthroscopy: The Journal of Arthroscopic & Related Surgery 2025;41(3):565 View
  11. Ho C, Tian T, Ayers A, Aaron R, Phillips V, Wolf R, Mathioudakis N, Dai T, Klonoff D. Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review. BMC Medical Informatics and Decision Making 2024;24(1) View
  12. Mo J, Chong D, Sun C, Mohapatra N, Jiam N. Machine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review. Ear & Hearing 2025 View

Books/Policy Documents

  1. Mehta U, Bagali K, Kommanapalli S. AI, Consciousness and The New Humanism. View