If you aren’t already using augmented intelligence (AI) in your healthcare settings, it could be happening soon.
According to an AMA study, physicians’ use of AI (commonly referred to as artificial intelligence) in certain tasks has nearly doubled in just one year. Approximately three in five physicians surveyed by the AMA in 2024 reported using healthcare AI for tasks such as documentation of billing codes, medical charts, consultation notes, and more. Preparing discharge instructions, care plans, and/or progress notes. Translation services, diagnostic support, etc.
The AMA survey (PDF) on physician sentiment toward healthcare AI also found that enthusiasm for the technology rose 5 points in just one year, with 35% reporting that their enthusiasm for healthcare AI outweighed their concerns. This is up from 30% who felt that way a year ago.
From implementing AI to EHR adoption and ease of use, the AMA strives to make technology work for physicians and make it an asset, not a burden, for physicians. As part of that effort, the AMA brought together CME to help physicians better understand the health AI technologies that are becoming part of the world.
AMA offers physicians the opportunity to explore seven AI modules that are part of the AMA Ed Hub™ CME series “AI in Health Care,” which introduces learners to the fundamental principles of AI and machine learning. Machine learning is a subdomain of AI that allows computers to learn patterns and relationships from data without being explicitly programmed by humans.
Developed by the AMA ChangeMedEd Initiative and the University of Michigan DATA-MD team, it is aimed at medical students and is also suitable for residents, fellows, practicing physicians, and other health care professionals.
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- Learn the basics of AI and how it is being developed for healthcare settings. The “Introducing Artificial Intelligence in Healthcare” module explains what big data is and how it impacts AI and machine learning in healthcare environments. Physicians can also learn how to distinguish between traditional computer programming, AI, and machine learning, and how to recognize the potential strengths and limitations of AI and machine learning.
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- Explore three basic machine learning techniques (supervised learning, unsupervised learning, and reinforcement learning) and learn how to apply supervised learning models to datasets to help solve clinical problems, such as predicting the presence or absence of cardiovascular disease. In the AI in Healthcare: Methodology module, physicians can also learn how to identify deep learning, such as convolutional neural networks and recurrent neural networks, and natural language processing used in healthcare.
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- Learn how AI-based diagnostic tools can complement physicians’ diagnostic decision-making. This module, Using AI in Diagnosis, describes the components of the diagnostic process and how AI and machine learning are currently being used in that process. Because each AI system has limitations, this module teaches physicians how to interpret diagnostic accuracy performance metrics commonly reported in the AI and machine learning literature and applies the results of diagnostic studies using AI and machine learning to clinical cases.
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- Through this module, “AI for Prognosis and Treatment,” you will learn how to recognize the potential uses of AI and machine learning in making prognosis and treatment decisions. Physicians can learn about the benefits and limitations of these technologies in prognosis prediction and treatment, how to interpret the results of prognostic research involving AI and machine learning, and how to apply the results of prognostic research involving AI and machine learning to clinical cases.
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- Understand what it takes to move AI from theory to reality in the clinic. “Practical Applications of AI in Health Systems” discusses the potential impact of AI on clinical practice, research, and education. We also identify AI stakeholders and their roles, outline important considerations when implementing AI and machine learning algorithms into clinical practice, and discuss the potential impact that AI and machine learning may have on various aspects of the healthcare system.
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- Find out how physicians can help ensure that AI is used ethically and safely in clinical practice. This module, “Navigating the Ethical and Legal Considerations of AI in Healthcare,” helps physicians become aware of the potential ethical and legal challenges associated with the use of AI and machine learning in healthcare. We also detail the laws and responsibilities related to healthcare AI and machine learning, and discuss the current governance and regulatory landscape.
These seven CME modules are durable materials and are rated up to 3.5 by the AMA. AMA PRA Category 1 Credit™.
They are part of the AMA Ed Hub, an online platform that provides high-quality CME and education to support the professional development needs of physicians and other healthcare professionals. It also provides an easy and streamlined way to search, capture, track, and report on your educational activities using topics that are relevant to you. Learn more about AMA CME certification.
The Ed Hub AI collection brings together even more healthcare AI learning modules, including research on how chatbots, large-scale language models, natural language processing, and machine learning are transforming medicine and healthcare.
Also, check out JAMA+ AI, a channel dedicated to the best science content, educational reviews, and commentary on AI and medicine. JAMA Network™ channels compile content published around the world. Japan Automobile Manufacturers Association®, JAMA network open and Japan Automobile Manufacturers Association Published in professional journals and based on their published content, new multimedia materials are added, such as author interviews, videos, medical news, and regular podcasts.
