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AI in medical physics: guidelines for publication

Issam El Naqa, John M. Boone, Stanley Benedict, Mitchell M. Goodsitt, Heang‐Ping Chan, Karen Drukker, Lubomir M. Hadjiiski, Dan Ruan, Berkman Sahiner

2021Medical Physics44 citationsDOIOpen Access PDF

Abstract

The Abstract is intended to provide a concise summary of the study and its scientific findings. For AI/ML applications in medical physics, a problem statement and rationale for utilizing these algorithms are necessary while highlighting the novelty of the approach. A brief numerical description of how the data are partitioned into subsets for training of the AI/ML algorithm, validation (including tuning of parameters), and independent testing of algorithm performance is required. This is to be followed by a summary of the results and statistical metrics that quantify the performance of the AI/ML algorithm.

Topics & Concepts

NoveltyStatement (logic)Computer scienceMedical physicsMedical physicistAlgorithmMachine learningData scienceData miningArtificial intelligencePhysicsPhilosophyPolitical scienceLawTheologyRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationHemodynamic Monitoring and Therapy
AI in medical physics: guidelines for publication | Litcius