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Course Outline
Introduction to AI in Healthcare
- Applications of AI in clinical decision support and diagnostics.
- Overview of healthcare data modalities: structured, text, imaging, and sensor data.
- Challenges unique to medical AI development.
Healthcare Data Preparation and Management
- Working with EMRs, lab results, and HL7/FHIR data.
- Medical image preprocessing (DICOM, CT, MRI, X-ray).
- Handling time-series data from wearables or ICU monitors.
Fine-Tuning Techniques for Healthcare Models
- Transfer learning and domain-specific adaptation.
- Task-specific model tuning for classification and regression.
- Low-resource fine-tuning with limited annotated data.
Disease Prediction and Outcome Forecasting
- Risk scoring and early warning systems.
- Predictive analytics for readmission and treatment response.
- Multi-modal model integration.
Ethics, Privacy, and Regulatory Considerations
- HIPAA, GDPR, and patient data handling.
- Bias mitigation and fairness auditing in models.
- Explainability in clinical decision-making.
Model Evaluation and Validation in Clinical Settings
- Performance metrics (AUC, sensitivity, specificity, F1).
- Validation techniques for imbalanced and high-risk datasets.
- Simulated vs. real-world testing pipelines.
Deployment and Monitoring in Healthcare Environments
- Model integration into hospital IT systems.
- CI/CD in regulated medical environments.
- Post-deployment drift detection and continuous learning.
Summary and Next Steps
Requirements
- A solid understanding of machine learning principles, particularly supervised learning.
- Experience working with healthcare datasets, such as EMRs, imaging data, or clinical notes.
- Proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorch).
Audience
- Medical AI developers.
- Healthcare data scientists.
- Professionals developing diagnostic or predictive healthcare models.
14 Hours
