Full course coming soon

Evaluating AI Systems for Scientific and Clinical Decisions

7 chapters · 32 lessons

1. 1. Framing Scientific Questions for Computational Analysis

   4 lessons

   Learn how to translate biological and clinical problems into well-defined questions that AI systems can meaningfully address.

2. 2. Assessing Data Quality and Provenance in Biotech Workflows

   5 lessons

   Develop skills to evaluate whether datasets are trustworthy, representative, and fit for purpose in scientific contexts.

3. 3. Validating Model Outputs with Domain Knowledge

   5 lessons

   Practice applying biological reasoning and statistical principles to determine whether AI results are plausible and actionable.

4. 4. Designing Validation Experiments and Reproducibility Checks

   4 lessons

   Learn how to structure follow-up experiments and documentation to confirm AI findings meet scientific standards.

5. 5. Translating Technical Results for Scientific Stakeholders

   5 lessons

   Build communication skills to present AI findings clearly to biologists, clinicians, and decision-makers.

6. 6. Navigating Regulatory and Compliance Requirements

   4 lessons

   Understand how regulatory frameworks shape AI deployment in clinical and healthcare-adjacent settings.

7. 7. Integrating AI into Biotech and Clinical Workflows

   5 lessons

   Learn how to design and implement AI systems that fit real-world research and clinical operations.