Improving Federal Regulation of Medical Algorithms
In emergency situations, doctors have little time to save the lives of trauma patients. Gunshot wounds, car crashes, and other life-threatening harms often cause severe blood loss, which is the leading cause of preventable death when trauma puts patients’ lives on the line.
To manage the demands of these emergency cases, physicians today complement their medical skill-set with a new tool: algorithms.
But in a recent paper, a legal scholar argues that federal regulatory reforms must occur to unleash the full lifesaving potential of algorithms in health care. Nicholson Price, a professor at University of Michigan Law School, claims that the U.S. Food and Drug Administration (FDA) lacks the necessary expertise in computer science to apply its current regulations to medical algorithms and, as a result, could discourage much-needed innovation.
Price asserts that the FDA will likely categorize medical algorithms as high-risk regulated medical devices because doctors will use them like pre-existing diagnostic tools that already fall into this risk category. Because of the high-risk nature of these medical devices, FDA requires premarket approval—the most stringent regulatory requirement for medical device licensing.
In addition to the expectation that medical devices go through clinical trials, premarket approval requires medical device developers to present thorough evidence about their products’ safety and efficacy to FDA. Regulations require approximately six months for premarket approval, but the process usually lasts longer than this baseline requirement.
For the development and marketing of medical algorithms, FDA’s stringent regulatory requirements pose important challenges, argues Price.
More than anything, FDA needs greater expertise in computer science. Medical algorithms depend on machine learning—a developing field of artificial intelligence that seeks to enable computers to learn information without human action. Developers design medical algorithms in ways that enable them to adapt to new data input. If a medical algorithm computes the ideal dose of antibiotics for a patient, for example, it can adjust its recommendation as soon as medical professionals add new data into their databases.
Because medical algorithms change over time autonomously and in ways their developers cannot predict, their codes become moving targets, argues Price. Premarket approval of medical algorithms thus requires deep expertise in machine learning, which FDA currently lacks.