Ethical Evaluation and Governance of Clinical AI in Healthcare

Description from DukeHub

This course examines the critical aspects of evaluating AI tools in healthcare, with a strong emphasis on ethical considerations and governance frameworks. Students will explore the complexities of identifying and addressing algorithmic bias and assessing its impact on healthcare outcomes and equity. The curriculum highlights governance mechanisms, including regulatory compliance, accountability, and the role of oversight bodies in mitigating bias and ensuring the fair and responsible use of AI in healthcare. By the end of the course, students will be prepared to evaluate clinical AI tools critically and contribute to the development and implementation of ethical AI practices in healthcare. Prerequisite: Permission only for MB students. Non-program students must obtain permission from the director of graduate studies or the instructor. Credits: 3

Skills

• data and statisticsdepartment: BIOSTAT
• ai and machine learningdescription: "ai" and 3 more
• financetitle: "evaluation"
• policy and lawdescription: "governance" and 2 more
• clinicaltitle: "clinical"

AI-resilient: 2 of 5 2/5

• No: Physical, lab or field work
• ✓Yes: Live work with people · "clinical"
• ✓Yes: Judgment someone is accountable for · "ethical"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in BIOSTAT

• Introduction to Statistical Theory and Methods II BIOSTAT 704
• Applied Biostatistical Methods II BIOSTAT 705
• Introduction to the Practice of Biostatistics II BIOSTAT 706
• Clinical Trial Design and Analysis BIOSTAT 708
• Introduction to Causal Inference BIOSTAT 709
• Analysis of Correlated and Longitudinal Data BIOSTAT 718

All BIOSTAT courses · All of Health professions