After two decades in healthcare technology, the author has seen numerous promising medtech initiatives fail. Given this, questions arise as to why artificial intelligence (AI) in healthcare will be any different. At present, the field is undergoing a pivotal period, with the rapid adoption and implementation of healthcare AI aligning with mandatory data codification and interoperability. This moment is the result of over three decades of work by healthcare, technology, and regulation experts. The surge in health data that healthcare teams struggle to manage further primes the field for large-scale AI benefits.
The author then explores some key questions around the recent flurry of AI guidelines and regulations released by worldwide regulatory bodies and physician groups. Among these are queries about common themes across these regulations, how to understand over 1200 pages of regulatory material, and the anticipated impact now and in the future.
Answering “Why now?”, the author lists regulatory needs and risk of misuse among reasons. The governance of AI must evolve to meet demand and ensure patient safety. Further, new legislation and guidelines form some boundaries for innovation, balancing patient-focused ethics with the need for rapid progress.
As AI becomes more prevalent in clinical practice, it’s becoming apparent that it will likely transition into a standard of care. Current regulations and guidelines indicate a maturity phase within clinical AI. They also provide oversight and frameworks during AI vendor selection, adoption, and deployment.
Hospital leaders employing AI can expect guidelines and regulations to help evaluate vendors, promote utility, and assess maturity. Concurrently, existing AI users can use these tools to scrutinize vendors and guide renewals, adhering to evolving standards.
Patients, the end users of healthcare systems, should experience improved outcomes through the appropriate application of AI, facilitated by careful vendor scrutiny. As AI is adopted and used consistently across healthcare systems, it can increasingly benefit patient care.
In terms of the common elements across regulations and guidelines, they typically unify around areas such as oversight, transparency, privacy, and security. Risk management is commonly highlighted, underlining the industry’s commitment to responsible AI development.
In sum, the proliferation of AI legislation and guidelines represents a turning point for healthcare. As AI develops towards becoming a standard of care, these laws and recommendations will crucially form a structure for responsible development and deployment. For health system leaders, compliance will not only ease vendor evaluations but will lead the way towards a future where AI ensures safety, addresses workforce shortages, and enhances patient outcomes through uniform and thoughtful implementation.
