Regulatory considerations for artificial intelligence technologies in GI endoscopy.

Artificial intelligence (AI) technologies in clinical medicine have become the subject of intensive investigative efforts and popular attention. In domains ranging from pathology to radiology, AI has demonstrated the potential to improve clinical performance and efficiency. In gastroenterology, AI has been applied on multiple fronts, with particular progress seen in the areas of computer-aided polyp detection (CADe) and computer-aided polyp diagnosis (CADx), to assist gastroenterologists during colonoscopy. As clinical evidence accrues for CADe and CADx, our attention must also turn toward the unique challenges that this new wave of technologies represent for the U.S. Food and Drug Administration and other regulatory agencies, who are tasked with protecting public health by ensuring the safety of medical devices. In this review, we describe the current regulatory pathways for AI tools in gastroenterology and the expected evolution of these pathways.

Incorporating patient-preference evidence into regulatory decision making.

BACKGROUND: Patients have a unique role in deciding what treatments should be available for them and regulatory agencies should take their preferences into account when making treatment approval decisions. This is the first study designed to obtain quantitative patient-preference evidence to inform regulatory approval decisions by the Food and Drug Administration Center for Devices and Radiological Health. METHODS: Five-hundred and forty United States adults with body mass index (BMI) ≥ 30 kg/m(2) evaluated tradeoffs among effectiveness, safety, and other attributes of weight-loss devices in a scientific survey. Discrete-choice experiments were used to quantify the importance of safety, effectiveness, and other attributes of weight-loss devices to obese respondents. A tool based on these measures is being used to inform benefit-risk assessments for premarket approval of medical devices. RESULTS: Respondent choices yielded preference scores indicating their relative value for attributes of weight-loss devices in this study. We developed a tool to estimate the minimum weight loss acceptable by a patient to receive a device with a given risk profile and the maximum mortality risk tolerable in exchange for a given weight loss. For example, to accept a device with 0.01 % mortality risk, a risk tolerant patient will require about 10 % total body weight loss lasting 5 years. CONCLUSIONS: Patient preference evidence was used make regulatory decision making more patient-centered. In addition, we captured the heterogeneity of patient preferences allowing market approval of effective devices for risk tolerant patients. CDRH is using the study tool to define minimum clinical effectiveness to evaluate new weight-loss devices. The methods presented can be applied to a wide variety of medical products. This study supports the ongoing development of a guidance document on incorporating patient preferences into medical-device premarket approval decisions.