Data Augmented Technology Assisted Medical Decision Making

Overview

Class Central Tips

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.

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 week 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 week 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 week 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.