Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints

Abstract

Artificial intelligence (AI) is a disruptive technology that involves the use of computerised algorithms to dissect complicated data. Among the most promising clinical applications of AI is diagnostic imaging, and mounting attention is being directed at establishing and fine-tuning its performance to facilitate detection and quantification of a wide array of clinical conditions. Investigations leveraging computer-aided diagnostics have shown excellent accuracy, sensitivity, and specificity for the detection of small radiographic abnormalities, with the potential to improve public health. However, outcome assessment in AI imaging studies is commonly defined by lesion detection while ignoring the type and biological aggressiveness of a lesion, which might create a skewed representation of AI's performance. Moreover, the use of non-patient-focused radiographic and pathological endpoints might enhance the estimated sensitivity at the expense of increasing false positives and possible overdiagnosis as a result of identifying minor changes that might reflect subclinical or indolent disease. We argue for refinement of AI imaging studies via consistent selection of clinically meaningful endpoints such as survival, symptoms, and need for treatment.