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

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 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 2024 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