Regulating Artificial Intelligence for a Successful Pathology Future

Abstract

Artificial intelligence (AI), a set of techniques aimed at approximating some aspect of human cognition using machines,1 promises to provide pathologists a tool to improve inefficiencies and inaccuracies in diagnosis and laboratory testing, and to facilitate an anticipated decreasing number of pathologists' ability to serve an increasing number of patients with timely diagnoses, even as those diagnoses exhibit an increasingly complicated molecular complexity.2–4 Pathology has long examined the use of AI in pathologic diagnosis5 and is already using AI. Papanicolaou test imaging, approved by the US Food and Drug Administration (FDA) for screening purposes, is now fully, if not widely, used.6,7 Other areas of cytopathology are investigating the use of AI for diagnosis,8 and AI will undoubtedly play a strong and central role in its continuing progress toward becoming fully automated.9–11 Artificial intelligence in pathology promises to outperform human beings in their assessment of the specific characteristics necessary for pathologic and radiologic diagnosis.9,12 Despite the initial hysteria,13–16 it is now clear that AI will be, at least for the foreseeable future, a tool for pathologists, not a replacement for pathologists, and it is evident that although AI's use will fundamentally alter pathology practice, it is perhaps pathologists' greatest opportunity to invest themselves more fully in their patients' care.3,7 Pathologists' success with AI, however, will depend to a large degree on the successful implementation of efficient AI governance and regulation.Current regulation of AI technology, still relatively nascent, includes some basic regulatory frameworks from the FDA.17,18 Currently, the Clinical Laboratory Improvement Amendments of 1988 (CLIA) has requirements for the use of artificial intelligence in pathology. Under CLIA, laboratory use of artificial intelligence is limited to developing algorithms prior to validation and implementation. Changing the rules of test performance after validation and implementation is generally not allowed without revalidation. Medical societies and other groups have also weighed in on AI regulation. The American Medical Association (AMA) has also examined artificial intelligence in health care, promoting the interaction of physicians with the federal regulatory regime, emphasizing patient privacy and confidentiality issues, and supporting an AMA policy regarding AI that can be continuously refined.19 Further, the Royal Australian and New Zealand College of Radiologists has drafted some ethical principles for AI in health care,20 and the Center for Data Innovation, a data, technology, and public policy think tank, has suggested that the United States develop a national strategy around artificial intelligence to maximize its value.21Mature discussion and strategizing of AI regulation is increasingly needed. It is evident that AI will increasingly be necessary to interpret the world, and will be integral to social, political, and business environments, making decisions for human beings.22 Yet, the development of AI, broadly speaking, has occurred substantially outside any regulatory environment.23 There are few state laws, essentially no court cases, and very little legal scholarship addressing AI regulation.23 The lack of much-needed legal regulatory scholarly examination and regulatory guidance should not be surprising. Artificial intelligence development, marketing, and use do not fit the traditional regulatory environment, which has long provided robust solutions for long-established regulatory needs, such as patent, trademark, and copyright law, tort liability, and research and development oversight.23The need for regulation of AI will continue to grow as it becomes increasingly integral to numerous technologies, including health care technology, with which people are progressively interacting. Yet effective and sufficient AI governance is likely to be stubbornly difficult to establish