Clinical AI Risk Management
Equip health service staff to assess, control and monitor the clinical risks of AI tools across their life cycle, from validation before use to incident response and ongoing evaluation.
Upcoming course. LMS delivery and enrolment are not open. Release dates will be confirmed.
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Planned learning outcomes
- Plan a proportionate clinical validation of an AI tool, including local silent testing before go-live
- Interpret model performance measures, including sensitivity, specificity, predictive values, calibration and subgroup results
- Identify and rate clinical safety risks, including false negatives, alert fatigue, automation bias and generative errors
- Design meaningful clinician oversight consistent with Ahpra obligations and health service governance
- Respond to and learn from clinical AI incidents, including evidence preservation and TGA reporting where relevant
- Set up ongoing evaluation with monitoring measures, triggers and change control
- Prepare a clinical AI risk assessment with a reasoned recommendation for a clinical governance committee