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

Digital Health Reporting Standards, Quality and Transparency in e-Research (132) Machine Learning (2076) Artificial Intelligence (2129) Digital Science, Open Science (91) Knowledge Translation and Implementation Science (513) Big Data (307) Advanced Data Analytics in eHealth (209)

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
Article Authors Cited by (38) Tweetations (3) Metrics

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;34(8):5028 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
  7. Khadhouri S, Hramyka A, Gallagher K, Light A, Ippoliti S, Edison M, Alexander C, Kulkarni M, Zimmermann E, Nathan A, Orecchia L, Banthia R, Piazza P, Mak D, Pyrgidis N, Narayan P, Abad Lopez P, Nawaz F, Tran T, Claps F, Hogan D, Gomez Rivas J, Alonso S, Chibuzo I, Gutierrez Hidalgo B, Whitburn J, Teoh J, Marcq G, Szostek A, Bondad J, Sountoulides P, Kelsey T, Kasivisvanathan V, Tijerina A, Simoes A, Ali A, Nic an Riogh A, Wong A, Kiciak A, Ridgway A, Dhanasekaran A, Cheong A, Atayi A, Ashpak A, Teixeira B, Maria Scornajenghi C, Marramaque C, Reynoldson C, Ho Chee Kong C, Crewe C, Griffiths D, Amporore D, Sarkar D, Chung Wei Ling D, Bheenick D, Orakwe D, Gordon E, Checcucci E, Ribeiro Gonçalves F, Lozano Palacio F, Prata F, Del Giudice F, Aggarwal G, Hatzichristodoulou G, Karagiannidis G, Maria Pirola G, Russo G, Hytham H, Chun Khoo H, Abozied H, Patel H, Colvin H, Ali I, Fakhradiyev I, Sokolakis I, Tsikopoulos I, Chong J, Abbaraju J, Hayes J, Luis Bauza Quetglas J, Antonio Herranz Yague J, Colombo Stenstrom J, de Mello K, Brodie K, Tzelves L, Lazaros L, Paramore L, Rico L, Lilis L, Ullmann M, Srour M, Boltri M, Mustafa M, Eyad Takahji M, Almusimie M, Shakeel Inder M, Elgamal M, Misurati M, Ali M, Binnawara M, Bhaloo N, Vidal Crespo N, Ernesto Morales Palacios N, Santoni N, Hamilton O, Maheshkumar P, Moreno P, Sarmah P, Matulewicz R, Contieri R, David R, Mohammad S, Abu S, Weber S, Abuhasanein S, Lee T, Klatte T, Trung Thanh T, Wazir U, Ulker V, Yeoh W, Feuer Z, Elahi Z, Gall Z. Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer. European Urology Focus 2024;10(6):1034 View
  8. Cai Y, Gong D, Tang L, Cai Y, Li H, Jing T, Gong M, Hu W, Zhang Z, Zhang X, Zhang G. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. Journal of Medical Internet Research 2024;26:e47645 View
  9. Trojan A, Laurenzi E, Jüngling S, Roth S, Kiessling M, Atassi Z, Kadvany Y, Mannhart M, Jackisch C, Kullak-Ublick G, Witschel H. Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning. Frontiers in Digital Health 2024;6 View
  10. Sabazade S, Lumia Michalski M, Bartoszek J, Fili M, Holmström M, Stålhammar G. Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs. Ophthalmology Science 2025;5(1):100613 View
  11. Speiser J, Kerr W, Ziegler A. Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods. Neurology 2024;103(7) View
  12. Koçak B, Keleş A, Köse F. Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?. Diagnostic and Interventional Radiology 2024;0(0):0 View
  13. Seas A, Zachem T, Valan B, Goertz C, Nischal S, Chen S, Sykes D, Tabarestani T, Wissel B, Blackwood E, Holland C, Gottfried O, Shaffrey C, Abd-El-Barr M. Machine learning in the diagnosis, management, and care of patients with low back pain: a scoping review of the literature and future directions. The Spine Journal 2025;25(1):18 View
  14. Kocak B, Ponsiglione A, Stanzione A, Ugga L, Klontzas M, Cannella R, Cuocolo R. CLEAR guideline for radiomics: Early insights into current reporting practices endorsed by EuSoMII. European Journal of Radiology 2024;181:111788 View
  15. Tan J, Quan L, Salim N, Tan J, Goh S, Thumboo J, Bee Y. Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation. JMIR AI 2024;3:e58463 View
  16. Allan-Blitz L, Ambepitiya S, Prathapa J, Rietmeijer C, Kularathne Y, Klausner J. Synergistic pairing of synthetic image generation with disease classification modeling permits rapid digital classification tool development. Scientific Reports 2024;14(1) View
  17. Dabbah S, Mishani I, Davidov Y, Ben Ari Z. Implementation of Machine Learning Algorithms to Screen for Advanced Liver Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease: An In-Depth Explanatory Analysis. Digestion 2024;106(3):189 View
  18. Yang X, Li Z, Lei L, Shi X, Zhang D, Zhou F, Li W, Xu T, Liu X, Wang S, Yuan Q, Yang J, Wang X, Zhong Y, Yu L. Noninvasive Oral Hyperspectral Imaging–Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study. Journal of Medical Internet Research 2025;27:e67256 View
  19. Maru S, Kuwatsuru R, Matthias M, Simpson Jr R. Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023). Journal of Medical Internet Research 2025;27:e60148 View
  20. Teles A, de Moura I, Silva F, Roberts A, Stahl D. EHR-based prediction modelling meets multimodal deep learning: A systematic review of structured and textual data fusion methods. Information Fusion 2025;118:102981 View
  21. Hornstein S, Lueken U, Wundrack R, Hilbert K. Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study. JMIR AI 2025;4:e63701 View
  22. Zmudzki F, Smeets R, Groenewegen J, van der Graaff E. Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation. JMIR Rehabilitation and Assistive Technologies 2025;12:e65890 View
  23. Schaye V, DiTullio D, Guzman B, Vennemeyer S, Shih H, Reinstein I, Weber D, Goodman A, Wu D, Sartori D, Santen S, Gruppen L, Aphinyanaphongs Y, Burk-Rafel J. Large Language Model–Based Assessment of Clinical Reasoning Documentation in the Electronic Health Record Across Two Institutions: Development and Validation Study. Journal of Medical Internet Research 2025;27:e67967 View
  24. Pallumeera M, Giang J, Singh R, Pracha N, Makary M. Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging. Cancers 2025;17(9):1510 View
  25. Svennberg E, Han J, Caiani E, Engelhardt S, Ernst S, Friedman P, Garcia R, Ghanbari H, Hindricks G, Man S, Millet J, Narayan S, Ng G, Noseworthy P, Tjong F, Ramírez J, Singh J, Trayanova N, Duncker D, Tfelt Hansen J, Barker J, Casado-Arroyo R, Chatterjee N, Conte G, Diederichsen S, Linz D, Mahtani A, Zorzi A. State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology. Europace 2025;27(5) View
  26. Cifuentes-González C, Gutiérrez-Sinisterra L, Rojas-Carabali W, Boon J, Hudlikar A, Wei X, Shchurov L, Oo H, Loh N, Shannon C, Rodríguez-Camelo L, Lee B, de-la-Torre A, Agrawal R. Novel Artificial Intelligence–Based Quantification of Anterior Chamber Inflammation Using Vision Transformers. Translational Vision Science & Technology 2025;14(5):31 View
  27. Pulick E, Curtin J, Mintz Y. Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study. JMIR Formative Research 2025;9:e73265 View
  28. Kalimouttou A, Stevens R, Pirracchio R. Harnessing AI in critical care: opportunities, challenges and key steps for success. Thorax 2025:thorax-2024-222125 View
  29. Iino H, Kizaki H, Imai S, Hori S. Construction of Personalized Predictive Models for Missed Medication Doses Using Wearable Device Data: Prospective Observational Study. JMIR Formative Research 2025;9:e72113 View
  30. Mussavi Rizi M, Fernández D, Kramer J, Saigal R, DiGiorgio A, Beattie M, Ferguson A, Kyritsis N, Torres-Espín A, Kumar P, Bresnahan J, Duong-Fernandez X, Hemmerle D, Huie J, Keller A, Lai N, Manley G, Pan J, Pascual L, Singh V, Talbott J, Torres-Espin A, Weinstein P, Whetstone W. Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury. npj Digital Medicine 2025;8(1) View
  31. Kopacheva E, Henriksson A, Dalianis H, Hammar T, Lincke A. Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study. JMIR Formative Research 2025;9:e71949 View
  32. Zhang X, Ferry J, Hewson D, Collins G, Wiles M, Zhao Y, Martindale A, Tomaschek M, Bowness J. Guidance for reporting artificial intelligence technology evaluations for ultrasound scanning in regional anaesthesia (GRAITE‐USRA): an international multidisciplinary consensus reporting framework. Anaesthesia 2025;80(12):1528 View
  33. Liu J, Ssewamala F, An R, Ji M. Use of Automated Machine Learning to Detect Undiagnosed Diabetes in US Adults: Development and Validation Study. JMIR AI 2025;4:e68260 View
  34. Lo P, Chan F, Ku Y, Wang S, Chen H. Development and validation of machine learning models to predict vancomycin- and teicoplanin-associated acute kidney injury: A retrospective, multicenter study. International Journal of Antimicrobial Agents 2026;67(1):107651 View
  35. Rubin D, Reinisch W, Narula N, Colucci D, Eastman W, Gottlieb K, Lacerda A, Laroux F, Modesto I, Navajas E, Owen C, Wang Y, Baxi S. Machine Learning Models for the Assessment of the Mayo Endoscopic Score in Ulcerative Colitis Trial Endpoints: A Systematic Review. Inflammatory Bowel Diseases 2025 View
  36. Khalafi M, Safavi-Naini S, Salehi A, Naderi N, Alijanzadeh D, Moghadam P, Kavousi K, Golestani N, Shahrokh S, Fallah S, Samaan J, Tatonetti N, Hoerter N, Nadkarni G, Aghdaei H, Soroush A. Vision language models versus machine learning models performance on polyp detection and classification in colonoscopy images. Scientific Reports 2025 View
  37. Cicerone O, Maestri M. Machine learning to predict metabolic-associated fatty liver disease. World Journal of Gastroenterology 2025;31(45) View

Conference Proceedings

  1. Ruback L, Felix L, Soares Teles A. Anais do XL Simpósio Brasileiro de Banco de Dados (SBBD 2025). Equitable Diabetes Diagnosis: Tackling Ethnic and Gender Disparities View