Jean-Christophe Bélisle-Pipon discusses the integration of artificial intelligence into healthcare and emphasizes the need for sustained ethical engagement to address AI’s deep-seated impacts.

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The transformation of artificial intelligence (AI) from a novel spectacle to a trivial tool is increasingly evident in the health sector. What was once heralded as a groundbreaking innovation capable of revolutionizing medical care has quietly integrated into the fabric of healthcare systems, often unnoticed by the very professionals it assists. This shift from spectacle AI (which gets people talking, which impresses, which makes headlines) to a trajectory of total AI mainstreaming across all sectors, has unique implications for healthcare, where the stakes are particularly significant.

AI’s entrance into healthcare began with high expectations. Early applications were met with both awe and skepticism, as AI systems demonstrated potential in tasks ranging from diagnosing diseases from imaging studies to predicting patient outcomes with more accuracy than seasoned clinicians. These early demonstrations promised a healthcare revolution driven by technology that could “think”—and “learn”—faster than humans. However, as certain AI technologies have matured (particularly in terms of image recognition and natural language processing), its use in healthcare has become more nuanced and, paradoxically, more trivialized. It’s not that AI’s potential utility has diminished or even plateaued, but rather that its applications have become more integrated, quietly less debated, and even far less visible and tangible in everyday medical practices. AI systems are now routinely used to analyze large datasets for research, manage patient information more efficiently, and support diagnostic processes with a level of precision that subtly blends into the workflow of healthcare providers.

Photo Credit: Tara Winstead/pexels. Image Description: An illustration of a robotic and human hand symbolizing artificial intelligence (AI), machine learning, and intelligent computing systems.

One of the most significant areas of AI application in healthcare is in diagnostic imaging. AI algorithms enhance the ability of radiologists to detect anomalies in X-rays, MRIs, and CT scans with greater accuracy and speed. These tools have become so integrated into diagnostic processes that they are often just another layer in the software suite, unnoticed unless absent. Similarly, AI-driven predictive analytics are used to forecast patient outcomes, tailor treatment plans, and manage hospital resources. These systems analyze historical data and ongoing patient information to make predictions about everything from disease progression, triage priority, to bed availability. This behind-the-scenes activity is seldom visible and surprisingly of little concern to certain segments of the patient community—while there is a certain mistrust or aversion of AI applications,  a growing body of work suggests that part of the patient population is rather indifferent on whether AI has been used or not, nor should it be disclosed to them—and by healthcare providers who benefit from the enhanced efficiency and accuracy it brings.

The trivialization of AI in healthcare is perhaps a sign of its success. Has AI yet become like electricity or the internet? A foundational technology that, while not always visible and tangible, is now indispensable? No matter what the answer, the fading novelty of AI in healthcare does not diminish its impact but rather indicates its seamless integration into the systems that depend on it. Concerns about “AI fatigue”—a diminishing concern for AI’s broader implications due to its routine and pervasive use—emerge. This fatigue risks dulling the critical engagement necessary to identify, discuss and address AI’s ethical, legal, and social implications (ELSI), which are especially pronounced in the health sector.  Examples of AI-related ELSIs include the potential for AI systems to perpetuate and even amplify existing healthcare inequalities due to biased training data, the erosion of clinician-patient trust due to AI-driven decisions that are not easily explainable, and the potential for AI to create new forms of dependency in healthcare systems underscore the need for maintaining ethical oversight to prevent the trivialization of AI’s impact on healthcare.

Here, bioethics must play a vital role in ensuring that this disinterest does not curtail meaningful discussions about AI’s influence. Bioethicists must keep these debates alive and connected to practical applications, ensuring they are not reduced to mere academic musings. To paraphrase Michael Sandel (on the importance of justice), we should ensure that AI bioethics isn’t just a spectator sport. Bioethicists’ engagement is crucial for ensuring that as AI technologies continue to evolve and permeate our lives, they are implemented within an ethical framework that is robust, comprehensible, and actively maintained. The increasing trivialization of AI in healthcare is a testament to its growing importance and utility in the sector. As AI continues to evolve from a wonder to a standard of practice, the true challenge will be in maintaining the delicate balance between utility and oversight.

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Jean-Christophe Bélisle-Pipon is an Assistant Professor in Health Ethics, Faculty of Health Sciences, Simon Fraser University.