Master Study AI

Ethics, Bias, and Fairness in Medical AI: Designing Trustworthy Healthcare Systems

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Course Modules:

Module 1: Foundations of Medical AI Ethics

Principles of biomedical ethics: autonomy, justice, beneficence, and non-maleficence

Unique ethical challenges in AI for healthcare

Overview of AI regulatory bodies and ethical guidelines

Module 2: Understanding Bias in Medical Datasets

Historical and demographic bias in EHRs and imaging datasets

Sampling bias, label bias, and measurement bias

Examples of AI harms in healthcare systems

Module 3: Fairness in Algorithms and Predictions

Defining fairness: demographic parity, equal opportunity, equalized odds

Measuring disparities in medical predictions

Ensuring fairness across race, gender, age, and comorbidities

Module 4: Mitigation Strategies and Best Practices

Fairness-aware machine learning

Reweighting, data balancing, adversarial debiasing

Participatory design and patient-centered AI development

Module 5: Transparency, Explainability, and Accountability

The role of interpretable models in clinical trust

Tools like SHAP, LIME, and Grad-CAM for explanation

Assigning responsibility in high-stakes AI deployment

Module 6: Capstone Project – Audit and Improve a Medical AI System

Choose a medical AI use case (e.g., triage, disease prediction)

Identify ethical risks and fairness gaps

Propose improvements and submit an ethical impact report

Tools & Technologies Used:

Python

Fairlearn, AIF360 for bias detection

SHAP, LIME for interpretability

Simulated or real datasets (e.g., MIMIC-III, eICU)

Target Audience:

AI developers working on medical systems

Healthcare professionals using or evaluating AI tools

Clinical researchers and ethicists

Public health policymakers and data regulators

Global Learning Benefits:

Ensure AI in healthcare serves all patients equitably

Identify and prevent harm from biased models and datasets

Build transparent, ethical systems that gain trust from users and patients

Prepare for regulatory audits and compliance with global health standards

 

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