Data Augmented Technology Assisted Medical Decision Making
Learn to use AI/ML for enhanced medical diagnoses. Explore ethical considerations and critically evaluate AI studies.
Learn to use AI/ML for enhanced medical diagnoses. Explore ethical considerations and critically evaluate AI studies.
This course teaches healthcare professionals how to effectively use AI and machine learning to augment diagnostic decision-making. Designed for medical students, residents, fellows, physicians, advanced practice providers, and nurses, it covers the crucial role, strengths, and limitations of AI/ML in evidence-based medicine. Students learn to evaluate machine learning studies for bias, apply ML outputs to diagnostic decisions, and identify legal and ethical issues in healthcare AI. The curriculum includes foundational biostatistics, critical appraisal of AI/ML studies, and practical skills for using AI to enhance the diagnostic process.
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What you'll learn
Describe the role, strengths, and limitations of AI/ML in evidence-based medical decision making
Evaluate machine learning studies for bias and systematic error
Apply machine learning study results and outputs to diagnostic decisions
Interpret statistical measures used to evaluate ML model performance
Critically appraise AI/ML studies and determine their clinical applicability
Identify legal and ethical issues in healthcare AI/ML use
Skills you'll gain
This course includes:
3 Hours PreRecorded video
18 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course introduces healthcare professionals to the use of AI and machine learning in medical decision-making. It covers the fundamentals of AI/ML in healthcare, including data analysis, model development, and evaluation. Students learn to critically appraise AI/ML studies, understand statistical measures used in model evaluation, and apply ML outputs to clinical decisions. The curriculum also addresses ethical and legal considerations in healthcare AI, including bias, privacy, and governance. By the end of the course, participants will be equipped to effectively use AI/ML technologies to augment their diagnostic processes while navigating the associated challenges and ethical considerations.
Introduction to Artificial Intelligence and Machine Learning
Module 1 · 2 Hours to complete
Foundational Biostatistics and Epidemiology in AI/ML for Health Care Professionals
Module 2 · 3 Hours to complete
Using AI/ML to Augment Diagnostic Decisions
Module 3 · 2 Hours to complete
Ethical and Legal Use of AI/ML in the Diagnostic Process
Module 4 · 2 Hours to complete
Fee Structure
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Financial Aid
Instructor
Innovator in Medical Education and AI Integration
Dr. Cornelius James is an Assistant Professor in the Departments of Internal Medicine, Pediatrics, and Learning Health Sciences at the University of Michigan. He is a practicing primary care physician specializing as both a general internist and pediatrician. Dr. James has held various educational roles throughout his career, earning recognition as one of the top teachers in the U-M Department of Internal Medicine multiple times. In 2022, he received the prestigious pre-clinical Kaiser Permanente Excellence in Teaching Award, underscoring his commitment to medical education.As a National Academy of Medicine (NAM) Scholar in Diagnostic Excellence, Dr. James is actively involved in developing the Data Augmented, Technology Assisted Medical Decision Making (DATA-MD) curriculum, which aims to equip healthcare professionals with skills to utilize artificial intelligence (AI) and machine learning (ML) in diagnostic processes. He leads efforts to create a web-based AI/ML curriculum for the American Medical Association (AMA). His research interests encompass clinical reasoning, implementation of AI/ML curricula across medical education, and integrating digital tools into clinical practice.
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