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

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