TABLE 2.

Principles of Clinical AI Product Evaluation

Principles	Questions to be addressed
Relevance	What specific medical problem is the AI application intended to solve? Is this intended for research or clinical purposes? Who is the end user?
Usability	How will the AI application be integrated into the clinical workflow? How was the model trained? Was the model trained with a well-balanced data set? Was VA data used in model training? Does the AI tool have interpretability?
Risk	What are the risks associated with implementation? What are the benefits of clinical adoption? Is there potential for bias? How will potential errors be addressed?
Regulatory	Does the AI tool comply with agency and VISN/facility regulations? Is the AI tool compliant with data protection and privacy standards for the VA? How will quality assurance be maintained?
Technical requirements	Will the AI tool meet the VA Office of Information and Technology requirements (ie, hardware specifications, cloud vs host, and network security issues)? Who will provide technical oversight and maintenance?
Financial	Does the clinical benefit justify cost? What is the licensing model? Is there appropriate sole source justification? What are maintenance costs?

Abbreviations: AI, artificial intelligence; VA, US Department of Veterans Affairs; VISN, Veterans Integrated Services Network.