Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48763, first published .
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation

Authors of this article:

William Klement1, 2 Author Orcid Image ;   Khaled El Emam1, 2 Author Orcid Image

Journals

  1. Kocak B, Akinci D’Antonoli T, Ates Kus E, Keles A, Kala A, Kose F, Kadioglu M, Solak S, Sunman S, Temiz Z. Self-reported checklists and quality scoring tools in radiomics: a meta-research. European Radiology 2024 View
  2. Cai Y, Cai Y, Tang L, Wang Y, Gong M, Jing T, Li H, Li-Ling J, Hu W, Yin Z, Gong D, Zhang G. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Medicine 2024;22(1) View
  3. Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering 2024;11(4):337 View
  4. Norris M, Obeid N, El‐Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. International Journal of Eating Disorders 2024;57(6):1357 View
  5. BaHammam A. Artificial Intelligence in Sleep Medicine: The Dawn of a New Era. Nature and Science of Sleep 2024;Volume 16:445 View
  6. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View