If you haven't used Augmented Intelligence (AI) in your medical practice, it could be quick.
In just a year, the use of physicians' AI (commonly referred to as artificial intelligence) has almost doubled for certain tasks, AMA research shows. Approximately three of the five doctors surveyed by the AMA in 2024 reported using medical AI in practice for tasks such as billing, medical charts and memo visits. preparation of discharge orders, care plans, and/or progress notes. Translation services, support diagnosis, etc.
The AMA Survey of Doctors' Feelings for Healthcare AI (PDF) also found that enthusiasm for technology has increased by 5 percentage points in just one year, with 35% reporting enthusiasm for Healthcare AI surpassing concerns. That rose from 30%, which I felt like that a year ago.
From AI implementation to EHR adoption and ease of use, AMA is fighting to make the technology work for physicians, ensuring it is an asset for physicians. As part of that effort, AMA has brought together CMEs to help physicians better understand health AI technologies that have become 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, introducing learners to the basic principles of AI and machine learning.
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, physicians and other medical professionals.
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- Learn the basics of AI and how AI is developed for the healthcare environment. The Introduction to Artificial Intelligence in Healthcare module explains what big data is and its impact on AI and machine learning in a healthcare environment. Doctors 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.
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- Explore three basic machine learning methods: monitoring, unsupervised, and reinforcement learning, and learn how to apply monitored learning models to datasets to solve clinical problems such as predicting the presence or absence of cardiovascular disease. The AI in Healthcare: Methodology module allows physicians to learn deep learning, including convolutional and recurrent neural networks.
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- Learn how AI-based diagnostic tools can complement physicians' diagnostic decisions. This module, “Using AI in Diagnosis,” explains the components of the diagnostic process and how AI and machine learning are currently being used. Due to the limitations of each AI system, the module teaches physicians how to interpret performance measures of diagnostic accuracy commonly reported in the AI and machine learning literature, and uses AI and machine learning to apply diagnostic research results to clinical cases.
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- Discover the potential recognition methods AI and machine learning use in making prognosis and treatment decisions through this module, AI for prognosis and treatment. Physicians can learn about the benefits and limitations of these technologies for prognosis and treatment, how to interpret the results of prognostic studies, including AI and machine learning, and how to interpret the results of prognostic studies, including AI and machine learning, into clinical cases.
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- Understand what is necessary to bring AI from theory to reality in your clinic. “Practical Applications of AI in Health Systems” identifies the potential impact of AI on clinical practice, research and education. It also identifies AI stakeholders and their roles, outlines key considerations when implementing AI and machine learning algorithms in clinical practice, and explains the impact that AI and machine learning can have on various aspects of the healthcare system.
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- Discover how physicians can contribute to ensuring that AI is used ethically and safely in clinical settings. This module “navigates the ethical and legal considerations of AI in healthcare” to help physicians recognize the ethical and legal challenges associated with using AI in healthcare and using machine learning. It also delves into laws and responsibilities related to healthcare AI and machine learning, explaining the current governance and regulatory landscape.
These seven CME modules are permanent materials and are specified by the AMA at a maximum of 3.5 AMA PRA Category 1 Credit™.
They are part of AMA ED HUB, an online platform with high quality CMEs and education that supports the professional development needs of physicians and other healthcare professionals. It also offers easy and streamlined ways to find, film, track and report educational activities as the topic is relevant to you. Learn more about AMA CME certification.
Find more healthcare AI learning modules in the ED HUB AI collection, including chatbots, large-scale language models, natural language processing, and research into how machine learning can transform medicine and healthcare.
And check out Jama+ AI, a channel dedicated to the best scientific content, educational reviews and commentary on AI and medicine. Jama®, Jama Network Open and Jama It is built on professional journals and published content using new multimedia materials, including author interviews, videos, medical news, and regular podcasts.
