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With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence. Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no‐fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care.

The Cardio-Ankle Vascular Index (CAVI) represents a promising index of arterial stiffness. However, neither the CAVI measure nor its measurement device, the VaSera, have undergone general testing in a North American clinical setting. To begin the process of collecting normal values in the USA, we studied 20 male and 28 female volunteers without reported cardiovascular or renal disease and no history of smoking. Their CAVIs, Ankle-Brachial Indices (ABIs), and 4-limb blood pressures were measured in 3 positions: supine, 7° Trendelenburg, and 7° Reverse Trendelenburg. In addition, the ABI function was validated against an established ABI measurement technique. Position was found to affect CAVI and other hemodynamic parameters, indicating that CAVI is not robust to slight positional variations. No differences were found in the blood pressure between arms or legs (interbrachial or interankle), supporting recent meta-analyses and studies but contradicting other work. This study represents an early step in bringing the VaSera device and its CAVI measurement into clinical practice.

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