In early 2025, two-thirds of doctors reported using artificial intelligence (AI) for a wide range of purposes, including “documentation of billing codes, medical charts, and visit notes; generating discharge instructions, care plans, and progress notes; providing translation services; supporting diagnostic decisions; and more.” Although the healthcare sector was initially hesitant to adopt AI, it has since accelerated its integration efforts and now implements AI technologies at twice the rate observed in other economic sectors. The escalating costs of healthcare have prompted the increased adoption of artificial intelligence, aimed at enhancing operational efficiency, optimizing resource utilization, and ultimately reducing expenditures.
AI in healthcare extends beyond addressing administrative inefficiencies, as regulator-approved applications, classified as Software as a Medical Device (SaMD), are already showing clinical promise; for example, one AI algorithm used in a U.S. mammography study improved breast cancer detection rates by 9.4% and reduced false positives by 5.7%. Ongoing research is exploring the efficacy of SaMD across fields such as dermatology, radiology, psychiatry, and personalized medicine, where AI’s capacity to process large datasets and continuously learn enhances diagnostic accuracy and enables more individualized treatment approaches.
Although artificial intelligence presents considerable potential for advancing the healthcare sector, it simultaneously generates substantial uncertainties, given that technological developments outpace the formulation and implementation of regulatory frameworks. According to Professor Dr. Heinz-Uwe Dettling, Partner, Ernst & Young Law GmbHand EY GSA Life Sciences Law Lead, this issue is often described as the ‘locked versus adaptive’ AI challenge; regulatory efforts are necessary, but the current regulations were not designed to keep up with the rapid pace of technological advancements like those seen in artificial intelligence.
In addition to ongoing uncertainties surrounding regulatory frameworks, AI remains inherently imperfect. A study conducted by Rutgers University demonstrated that AI algorithms can inadvertently perpetuate erroneous assumptions, largely because they rely on datasets that may result in broad generalizations about people of color. Furthermore, these algorithms often neglect essential social determinants of health, such as transportation accessibility, the cost of nutritious food, and variable work schedules, which play a critical role in influencing patients’ capacity to comply with treatment regimens requiring frequent medical appointments, physical activity, and other health-related interventions.
Concerns regarding the implementation of artificial intelligence in healthcare have prompted regulators, legislators, and healthcare practitioners to call for the development of more comprehensive regulations and guidelines within this dynamically evolving sector. A thorough understanding of biases inherent in traditional education and healthcare professionals is essential, requiring developers to have both domain-specific knowledge and technical expertise. Additionally, implementing more rigorous processes to review data inputs is crucial to preventing biases in algorithms that may exacerbate healthcare disparities.
Because AI touches every part of the healthcare system, it is essential to have cross-agency coordination as well as regulations at the state and federal levels. Currently, multiple federal agencies regulate AI in healthcare, including the FDA, the Department of Health and Human Services (HHS), and the Centers for Medicare and Medicaid Services (CMS). In addition, states have enacted legislation designed to ensure that artificial intelligence remains a tool, not a replacement, in the doctor’s office. These state-level regulations require “healthtech” companies to embed compliance measures from the earliest stages of product development, including conducting thorough audits and employing geofencing technologies to navigate the patchwork of differing state laws effectively. By prioritizing proactive compliance and transparent practices, companies can not only mitigate legal risks but also build greater public trust, thereby enabling smoother adoption and competitive advantage in an increasingly regulated and scrutinized market.
