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.
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Data Augmented Technology Assisted Medical Decision Making
Instructeur : Cornelius James
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Ce que vous apprendrez
Describe the crucial role, strengths, limitations of AI and ML in evidence-based medical decision making
Evaluate machine learning studies for bias and systematic error to enhance diagnostic decisions.
Apply the results of machine learning studies and outputs to diagnostic decisions.
Identify legal and ethical issues and best practices for AI and ML use in healthcare settings
Compétences que vous acquerrez
- Catégorie : AI Law and Ethics
- Catégorie : AI for Diagnosis
- Catégorie : AI/ML Methodologies
- Catégorie : Medical Decision Making
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Il y a 4 modules dans ce cours
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.
Inclus
17 vidéos6 lectures5 devoirs1 sujet de discussion
In Module 2 you will learn the concepts and statistical measures necessary for interpretation of results of diagnostic studies that include ML.
Inclus
15 vidéos3 lectures5 devoirs1 sujet de discussion
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.
Inclus
14 vidéos3 lectures2 devoirs1 sujet de discussion
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.
Inclus
15 vidéos4 lectures6 devoirs1 sujet de discussion
Instructeur
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Recommandé si vous êtes intéressé(e) par Machine Learning
University of Glasgow
Johns Hopkins University
Duke University
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Foire Aux Questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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