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March 21, 2022

Preparing Clinicians for a Clinical World Influenced by Artificial Intelligence

Author Affiliations
  • 1Departments of Internal Medicine and Pediatrics, University of Michigan, Ann Arbor
  • 2Department of Internal Medicine, University of California, San Francisco
  • 3Departments of Internal Medicine and Learning Health Sciences, University of Michigan, Ann Arbor
JAMA. 2022;327(14):1333-1334. doi:10.1001/jama.2022.3580

Artificial intelligence (AI) and machine learning (ML) are poised to transform the way health care is delivered. AI is the use of computers to simulate intelligent tasks typically performed by humans. ML is a domain of AI that involves computers automatically learning from data without a priori programming. While AI has been critiqued as being in its “hype cycle” (throughout this article, AI will be used as shorthand for AI and ML), over time, it is likely that every medical specialty will be influenced by AI, and some will be transformed.1 As AI takes on a larger role in clinical practice, it is clear that multiple levels of oversight are needed. However, even with appropriate outside oversight, the importance of clinician review and trust of these technologies cannot be overstated. This Viewpoint outlines steps that could allow clinicians to be engaged and invested participants in health care that includes AI.

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1 Comment for this article
Focusing on Health Care's Purpose
Hyun Ko, MD | Peter MacCallum Cancer Centre, Melbourne Australia
This article is an excellent viewpoint of what humans and especially clinicians need to be aware of. In a recent blog post I shared similar sentiments from a more radiological and oncological perspective.

However, AI has a higher potential impact on healthcare and likely will replace human clinician reasoning in the near future. When this will happen is still unknown but we need to ensure that this transition will happen in a safe and ethical manner.

We must develop (rigorously train, validate and test) AI systems that will be free of (un)intentional bias and that will
be failsafe as well as ethically sound.

Future AI systems will be more complex and independent; they will not only be a mere adjunct tool but will be capable to teach themselves beyond human expert levels.

This is a tremendous opportunity for technological advancement and clinical operational paradigm shift we clinicians need to be aware of.

Medical schools and healthcare providers must prepare and inform the new generation of clinicians for AI systems to serve humankind in general and not just some privileged.

AI's purpose should be to help prevent disease and improve patient outcomes. This focus needs to be maintained always.