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Report No. 273 - AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging (2023) Category: Reports Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both “traditional” machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Medical Physics https://doi.org/10.1002/mp.16188 Altmetrics for this report Keywords: AI, best practices, CAD, decision support systems, image analysis, machine learning, medical Imaging, model development, reference standards Task Group No. 273 - CAD Assessment, Quality Assurance and Training (TG273) Lubomir Hadjiiski, Kenny Cha, Heang-Ping Chan, Karen Drukker, Lia Morra, Janne J. Nappi, Berkman Sahiner, Hiroyuki Yoshida, Quan Chen, Thomas Deserno, Hayit Greenspan, Henkjan Huisman, Zhimin Huo, Richard Mazurchuk, Nicholas Petrick, Daniele Regge, Ravi Samala, Ronald M. Summers, Kenji Suzuki, Georgia Tourassi, Daniel Vergara, Samuel G. Armato III Committee Responsible: Computer Aided Image Analysis Subcommittee Last Review Date: |
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