Overview

Artificial intelligence (AI) and machine learning (ML) have the potential to increase diagnostic accuracy, decrease diagnostic errors, and improve patient outcomes. The Data Augmented, Technology Assisted Medical Decision Making (DATA-MD) course will teach you how to use AI to augment your diagnostic decision-making. The National Academy of Medicine (NAM) recommends ensuring that clinicians can effectively use technology - including AI - to improve the diagnostic process. To use these technologies effectively in your clinical practice, you will need to determine when use of AI is appropriate, interpret the outputs of AI, read medical literature about AI, and explain to patients the role that AI plays in their care. In this course, you’ll explore the ethical considerations and potential biases when making medical decisions informed by AI/ML-based technologies. DATA-MD is a one of a kind curriculum designed to provide an introduction to the use of AI in the diagnostic process. This course was created with the needs of medical students, residents, fellows, practicing physicians, advanced practice providers, and registered nurses in mind. Others, like educators, computer programmers, and data scientists, may also find value in the course. Continuing Medical Education Information: This activity is released for CME credit on 07/30/2024 and expires 06/31/2027. The University of Michigan Medical School is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The University of Michigan Medical School designates this enduring material for a maximum of 3.5 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Dr. Cornelius James and Jessica Virzi, planner and co-planner for this educational activity, have no relevant financial relationship(s) with ineligible companies to disclose. Maggie Makar, Benjamin Li, and Nicholson Price, presenters of this educational activity, have no relevant financial relationship(s) with ineligible companies to disclose. Karandeep Singh, presenter for this educational activity, was a consultant for Flatiron Health. The relevant financial relationship listed for this individual has been mitigated. Cheri Breadon and Jessica Virzi are the coordinators for this activity. After this activity, participants will be able to -Use AI to augment your diagnostic clinical decision-making -Describe the strengths and limitations of AI/ML-based technology in the diagnostic process -Interpret statistical measures frequently used to evaluate the performance of ML models -Critically appraise studies that include AI/ML and determine the applicability of study results in clinical practice If you would like to earn CME credit for participating in this course, please review the information, including expected results, presenters, their disclosures, and CME credit at this website prior to beginning the activity: https://umich.cloud-cme.com/course/courseoverview?P=0&EID=61826

Syllabus

Introduction to Artificial Intelligence and Machine Learning

In week 1, you will be introduced to artificial intelligence (AI) and machine learning (ML) and the vocabulary necessary to effectively communicate with relevant stakeholders. You will learn about some of the applications of AI/ML in healthcare and the challenges associated with using these technologies in healthcare.

Foundational Biostatistics and Epidemiology in AI/ML for Health Care Professionals

In Module 2 you will learn the concepts and statistical measures necessary for interpretation of results of diagnostic studies that include ML.

Using AI/ML to Augment Diagnostic Decisions

In Module 3, you will develop the skills necessary to critically evaluate diagnostic studies that include AI/ML. This week emphasizes the skills necessary to efficiently and effectively use AI/ML to augment diagnostic decisions. step.

Ethical and Legal Use of AI/ML in the Diagnostic Process

In the final Module of this course, you will review the current legal and ethical landscape of AI/ML in medicine, possible social biases that may be perpetuated by AI/ML algorithms, and recommendations for avoiding these.