Roxana Daneshjou1,2, Catarina Barata3, Brigid Betz-Stablein4, M Emre Celebi5, Noel Codella6, Marc Combalia7, Pascale Guitera8,9, David Gutman10, Allan Halpern11, Brian Helba12, Harald Kittler13, Kivanc Kose11, Konstantinos Liopyris14, Josep Malvehy7, Han Seung Seog15,16, H Peter Soyer4, Eric R Tkaczyk17,18,19, Philipp Tschandl13, Veronica Rotemberg11. 1. Stanford Department of Dermatology, Stanford School of Medicine, Redwood City, California. 2. Stanford Department of Biomedical Data Science, Stanford School of Medicine, Stanford, California. 3. Institute for Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal. 4. The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Australia. 5. Department of Computer Science and Engineering, University of Central Arkansas, Conway. 6. Microsoft, Seattle, Washington. 7. Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain. 8. Melanoma Institute Australia, the University of Sydney, Camperdown, Australia. 9. Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, Australia. 10. Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia. 11. Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York. 12. Kitware, Inc, Clifton Park, New York. 13. Department of Dermatology, Medical University of Vienna, Vienna, Austria. 14. University of Athens Medical School, Athens, Greece. 15. Department of Dermatology, I Dermatology Clinic, Seoul, Korea. 16. IDerma, Inc, Seoul, Korea. 17. Dermatology Service and Research Service, Tennessee Valley Healthcare System, Department of Veterans Affairs, Nashville. 18. Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee. 19. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.
Abstract
IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety. OBJECTIVE: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI. EVIDENCE REVIEW: In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for "artificial intelligence" and "reporting guidelines," as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus. FINDINGS: A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology. CONCLUSIONS AND RELEVANCE: Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.
IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety. OBJECTIVE: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI. EVIDENCE REVIEW: In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for "artificial intelligence" and "reporting guidelines," as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus. FINDINGS: A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology. CONCLUSIONS AND RELEVANCE: Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.
Authors: Roxana Daneshjou; Kailas Vodrahalli; Roberto A Novoa; Melissa Jenkins; Weixin Liang; Veronica Rotemberg; Justin Ko; Susan M Swetter; Elizabeth E Bailey; Olivier Gevaert; Pritam Mukherjee; Michelle Phung; Kiana Yekrang; Bradley Fong; Rachna Sahasrabudhe; Johan A C Allerup; Utako Okata-Karigane; James Zou; Albert S Chiou Journal: Sci Adv Date: 2022-08-12 Impact factor: 14.957