Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint

Abstract

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Ethical issues of the use of AI-driven mobile apps for education

“…However, we cannot see these fields as necessarily strictly separated, but rather as overlapping or intertwined. This is especially so because interdisciplinary collaboration is seen as highly beneficial in the field of AI ethics as it is repeatedly supported by various research findings [see e.g., (14,(18)(19)(20)(21)(22)(23)(24)]. Engineering primarily encompasses the field of AI system manufacturing and includes all those involved in the entire AI .…”

“…The following studies have been yielded as they contain a clear systematic approach to the topic and they fully focus on the ethical issues of AI in mobile apps (14,(18)(19)(20)(21)(22)(23)(24). The other detected texts only included more or less superficial comments on ethics, touching only on isolated issues, such as privacy [e.g., (25)(26)(27)(28)].…”

“…The chosen studies can be further subdivided into those that address purely educational issues related to AI ( 19 , 21 , 22 , 24 ) and those that discuss the use of AI in medicine ( 20 , 23 ) and could also be included in this study, while Leimanis and Palkova ( 18 ) address ethical issues of AI in medical education. The remaining study ( 14 ) deals with the ethics of AI in various disciplines, with machine ethics, a subfield of the ethics of AI, as a focal point but it provides insightful comments related to education, therefore, it also had to be included in this review.…”

“…The most widespread ethical theories are mentioned in at least one of the examined texts. These include deontology ( 14 , 20 , 21 ), utilitarianism [( 14 , 20 , 21 ), consequentialism ( 14 ), virtue ethics ( 14 , 21 )]. These conceptual clarifications seem relevant and provide the reader with a necessary theoretical background that can be further extended into a practical application of this theoretical framing.…”

“…The texts studied bring some remarkable findings that seem to be very important to the understanding of the current situation. First, the most significant finding, included in six studies ( 14 , 18 – 20 , 23 , 24 ), is that interdisciplinary, multi-stakeholder collaboration that takes into account different perspectives across disciplines is essential for establishing globally acceptable standards of AI ethics. These stakeholders include end-users and developers of AI systems, as well as other organizational and societal stakeholders, including manufacturers and indeed anyone who comes into contact with AI systems ( 23 ).…”