Artificial intelligence (AI) systems, powered by streams of data and advanced algorithms, are improving healthcare by preventing hospitalizations, reducing complications, decreasing administrative burdens, and improving patient engagement. AI systems offer the promise to further accelerate and scale such results while providing the impetus for the ongoing transition from our current disease-based system to one centered upon prevention and health maintenance. Nonetheless, AI in healthcare also brings with it a variety of unique considerations for U.S. policymakers, particularly for medical device regulators like the Food and Drug Administration (FDA).
Many organizations are taking steps to proactively address the adoption and integration of AI into healthcare along with guidelines for how clinicians, technologists, patients and consumers, policymakers, and other stakeholders should approach this integration. Building on these important efforts, the Connected Health Initiative’s (CHI) AI Task Force has taken the next step to address the role of AI in healthcare through the development of health AI policy principles.
With this in mind, CHI’s AI Task Force developed good machine learning practices (GMLPs) as a baseline for the FDA, and other governmental and non-governmental stakeholders, to leverage in their ongoing consideration of the topic. CHI intends for this document to serve as a next step in shaping health AI-related policy developments at the FDA, at the U.S. federal level widely, and internationally.
Read the full white paper, “Machine Learning and Medical Devices: Connecting practice to policy (and back again)” by Sebastian Holst of Qi-fense with contributions by CHI’s Morgan Reed and Brian Scarpelli.
