Table 1.

Artificial Intelligence in Imaging

Best practices	Description
Clinical problem addressed with imaging is defined in consultation with clinical experts, AI/ML experts, and experts in ethics and patient engagement	Universal clinical adoption of AI/ML-based imaging tools needs proper definition of the clinical problem and needs alongside consideration of ethical issues with such use of these tools as well as a patient’s perspective on the utility and effect of these tools.
Study design, methods, and resultant AI/ML techniques used in developing imaging solutions are defined a priori	Formulation of a priori hypothesis, study aims, and objectives and appropriate study designs and reporting measures are key when assessing algorithm quality and validity.
Imaging data are adequate, representative, well characterized, and reusable	Data annotation uses well-defined rules (eg, medical imaging data readiness [MIDaR] and Findability, Accessibility, Interoperability, and Reusability [FAIR] principles) that also takes into consideration interrater variability.
Gaps and challenges	Description
Define disease states for which AI/ML-based image classification is validated	Diagnostic accuracy reflects pathophysiology, patient demographics, or technical issues with respect to data representation (ie, bias and lack of generalizability).
Identify imaging systems that may detect stroke	AI/ML-based and computational imaging algorithms may predict stroke, using diverse imaging modalities such as cardiac MRI, strain, or nuclear imaging.
Lack of representative imaging data sets	Imaging data from clinical repositories may have class imbalances and other biases (eg, data coming from highly selected centers).
Lack of studies that test effect on clinical outcomes	Most AI/ML algorithms have been tested on retrospective data, with minimal prospective practical clinical workflow development and testing demonstrating utility.

AI indicates artificial intelligence; ML, machine learning; and MRI, magnetic resonance imaging.