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.

Benefit-risk paradigm for clinical trial design of obesity devices: FDA proposal.

Diet and exercise, except in controlled circumstances, have not been shown to provide effective and prolonged weight loss for the majority of those who are obese. Several older drugs intended to reduce weight have been withdrawn from the market, and the new drugs show only modest weight loss. Surgical intervention, specifically procedures that alter the normal gastrointestinal anatomy, does provide prolonged periods of sustained weight loss, with rebound weight gain over time. A variety of medical devices to assist in weight reduction have been studied, but only two are legally marketed devices for obesity. The authors propose a new paradigm for devices intended to treat obesity, based on a benefit-risk determination, with the hope to provide sponsors an a priori tool for systematic assessment of the risks associated with the devices intended for treatment of obesity and to suggest appropriate levels of benefit for devices with different risk levels. The paradigm is not intended to determine the class of a device from a regulatory perspective. This approach was conceived at a Food and Drug Administration (FDA) co-sponsored workshop in October, 2011 and formally presented to an FDA advisory panel for discussion in May 2012.