JMIR Publications

ABSTRACT

With the explosion of innovation driven by generative and traditional AI, comes the necessity to understand and regulate products that often defy current regulatory classification. Tradition, and lack of regulatory expediency, imposes the notion of force fitting novel innovations into pre-existing product classifications or into the essentially unregulated domains of wellness and/or consumer electronics. Further, regulatory requirements, levels of risk tolerance, and capabilities vary greatly across the spectrum of technology innovators. For example, currently unregulated information and consumer electronic suppliers set their own editorial and communication standards without extensive federal regulation. However, industries like biopharma companies are held to a higher standard in the same space given current direct to consumer regulations that govern the interactions between biopharmaceutical companies, healthcare providers and patients, like the Sunshine Act, the federal Anti-Kickback Statute (AKS), the federal False Claims Act (FCA) and others. Clear and well-defined regulations not only reduce ambiguity but facilitate scale, showcasing the importance of regulatory clarity in fostering innovation and growth. To avoid highly regulated industries like healthcare and biopharma from being discouraged from developing AI to improve patient care, there is a need for a specialized framework to establish regulatory evidence for AI-based medical solutions. In this paper, we review the current regulatory environment considering current innovations but also pre-existing legal and regulatory responsibilities of the biopharma industry and propose a novel, hybridized approach for the assessment of novel AI-based patient solutions. Further, we will elaborate the proposed concepts via real and simulated case studies. This paper explores the challenges posed by the current regulatory environment, emphasizing the need for a specialized framework for AI-based medical solutions. By reviewing existing regulations and proposing a hybridized approach, we aim to ensure that the potential of AI in biopharmaceutical innovation is not hindered by uneven regulatory landscapes.