Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI application within radiology, known as radiomics, can provide detailed quantifications of the radiographic characteristics of underlying tissues. This information can be used throughout the clinical care path to improve diagnosis and treatment planning, as well as assess treatment response. This tremendous potential for clinical translation has led to a vast increase in the number of research studies being conducted in the field, a number that is expected to rise sharply in the future. Many studies have reported robust and meaningful findings; however, a growing number also suffer from flawed experimental or analytic designs. Such errors could not only result in invalid discoveries, but also may lead others to perpetuate similar flaws in their own work. This perspective article aims to increase awareness of the issue, identify potential reasons why this is happening, and provide a path forward. Clin Cancer Res; 24(3); 532–4. ©2017 AACR.
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