📘 Course 4: Evaluations of AI Applications in Healthcare #
| [ToC] Course 4 |
🤖 Module 1: AI in Healthcare #
1. What’s the Problem?
Understanding the growing role of AI in healthcare and what meaningful problems it can solve.
2. Why Does It Matter?
AI is being rapidly adopted, yet many applications focus on accuracy without considering clinical utility or impact.
3. What’s the Core Idea?
AI spans biomedical research, translational science, and medical practice. It excels at data synthesis and task automation, but needs alignment with care goals.
4. How Does It Work?
AI systems analyze large-scale structured and unstructured data to assist in diagnostics, treatment recommendations, and patient monitoring. Evaluating AI must go beyond accuracy to include actionability.
5. What’s Next?
Learn how to evaluate AI solutions based on clinical utility, outcome-action pairing, and feasibility of implementation.
🧪 Module 2: Evaluations of AI in Healthcare #
1. What’s the Problem?
Most AI models are evaluated only by accuracy, without considering whether they actually improve clinical care.
2. Why Does It Matter?
Because real-world deployment requires understanding how an AI model leads to meaningful actions and patient benefit.
3. What’s the Core Idea?
Use the Outcome-Action Pairing (OAP) framework: pair predictions with feasible actions that impact care. Evaluate utility, feasibility, and clinical impact.
4. How Does It Work?
Start with the clinical problem, define output and required action, assess lead time, type of action (medical, operational), and stakeholder involvement.
5. What’s Next?
Examine the four phases of deploying AI in clinical environments, from design to monitoring.
🚀 Module 3: AI Deployment #
1. What’s the Problem?
Even well-performing AI models often fail to reach or impact clinical care due to poor integration and lack of support.
2. Why Does It Matter?
Healthcare settings require safety, validation, stakeholder buy-in, and long-term monitoring for AI success.
3. What’s the Core Idea?
Deployment includes four stages: design and development, evaluation and validation, diffusion and scaling, and continuous monitoring.
4. How Does It Work?
Evaluate utility and economic value, run ‘silent mode’ trials, ensure human-machine interaction, and plan for infrastructure and updates.
5. What’s Next?
Understand how fairness, transparency, and bias affect AI performance across diverse populations.
⚖️ Module 4: Downstream Evaluations: Bias and Fairness #
1. What’s the Problem?
AI models may perpetuate bias or perform poorly on underrepresented populations.
2. Why Does It Matter?
Bias in AI can lead to inequities in care and further marginalize already vulnerable groups.
3. What’s the Core Idea?
Bias can occur at any stage—from data collection to deployment. Fairness requires proactive evaluation using tools like MINIMAR.
4. How Does It Work?
Identify and mitigate types of bias (e.g., representation, measurement, aggregation). Use fairness definitions like anti-classification and calibration.
5. What’s Next?
Explore how AI regulation addresses these concerns through risk frameworks and transparency standards.
📜 Module 5: Regulatory Environment for AI in Healthcare #
1. What’s the Problem?
AI products often lack regulatory approval due to unclear pathways and concerns over safety and accountability.
2. Why Does It Matter?
Regulation ensures AI tools are safe, effective, and beneficial in real-world healthcare settings.
3. What’s the Core Idea?
The FDA and IMDRF provide frameworks based on risk classification and clinical evaluation (valid association, analytical & clinical validation).
4. How Does It Work?
Follow the SaMD lifecycle: define intended use, evaluate risk, seek approval via 510(k), De Novo, or PMA, and ensure continuous monitoring.
5. What’s Next?
Apply ethical practices in problem formulation, data choice, stakeholder transparency, and conflict of interest management.
🧭 Module 6: Best Ethical Practices in AI for Healthcare #
1. What’s the Problem?
Conflicts of interest and unclear problem framing can undermine trust and effectiveness in AI systems.
2. Why Does It Matter?
Ethical lapses can lead to harm, inequity, or misuse of AI tools in sensitive healthcare decisions.
3. What’s the Core Idea?
Ethical AI requires clear goals, clinician input, transparent data use, and conflict-of-interest management.
4. How Does It Work?
Ask ethical questions early, assess biases in data and design, disclose secondary interests, and implement oversight.
5. What’s Next?
Adopt frameworks like MINIMAR, audit for fairness, and ensure that systems are explainable, justifiable, and trustworthy.