Regulatory Aspects of Artificial Intelligence and Machine Learning

Abstract

In the realm of health care, numerous generative and nongenerative artificial intelligence and machine learning (AI-ML) tools have been developed and deployed. Simultaneously, manufacturers of medical devices are leveraging AI-ML. However, the adoption of AI in health care raises several concerns, including safety, security, ethical biases, accountability, trust, economic impact, and environmental effects. Effective regulation can mitigate some of these risks, promote fairness, establish standards, and advocate for more sustainable AI practices. Regulating AI tools not only ensures their safe and effective adoption but also fosters public trust. It is important that regulations remain flexible to accommodate rapid advances in this field to support innovation and also not to add additional burden to some of our preexisting and well-established frameworks. This study covers regional and global regulatory aspects of AI-ML including data privacy, software as a medical device, agency approval and clearance pathways, reimbursement, and laboratory-developed tests.

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