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Advisory Areas

Five focused review services — each delivered through a socio-technical lens that considers data, models, people, workflows, and governance together.

Explore My Approach to Health AI Governance Review
01

Regulatory Compliance Review

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Focus of Review

  • Intended use and risk classification review
  • Governance roles and accountability check
  • Review of validation and safety documentation
  • Monitoring and incident reporting readiness

Governance Value

  • Clear view of compliance readiness
  • Early identification of approval and procurement risks
  • Reduced regulatory and legal exposure
  • Clear actions needed to meet governance requirements
02

Technical Readiness Review

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Focus of Review

  • Identification of technical limitations
  • Review of training and validation data quality
  • Assessment of model performance and stability
  • Evaluation of testing methods
  • Review of explainability and technical documentation

Governance Value

  • Confidence in technical reliability
  • Early detection of performance or data issues
  • Clear understanding of model limitations
  • Actionable recommendations for improvement
03

Clinical Workflow Integration Review

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Focus of Review

  • Mapping of AI use within clinical workflows
  • Review of decision points and handover processes
  • Assessment of role clarity and accountability
  • Evaluation of impact on workflow and safety
  • Identification of workflow risks and misalignment

Governance Value

  • Safer integration into real clinical practice
  • Reduced workflow disruption
  • Improved clinician acceptance
  • Clear guidance for operational deployment
04

Human-AI Interaction and Usability Review

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Focus of Review

  • Evaluation of user interface design and usability
  • Assessment of interpretability and clarity of AI outputs
  • Review of cognitive load, trust calibration, and user reliance
  • Identification of misuse or over-reliance risks
  • Review of training and user support needs

Governance Value

  • Safer clinician-AI interaction
  • Reduced risk of human error linked to poor AI design
  • Improved clinician understanding and appropriate use of AI outputs
  • Better trust calibration between humans and AI
  • Enhanced adoption and real-world effectiveness
05

Fairness and Bias Review

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Focus of Review

  • Review of dataset representativeness across patient populations
  • Assessment of model performance across demographic subgroups
  • Identification of bias and equity risks
  • Review of data collection and labelling practices
  • Recommendations for bias mitigation and monitoring

Governance Value

  • Reduced risk of unintended harm to specific populations
  • Improved transparency around AI limitations and risks
  • Stronger alignment with ethical and responsible AI principles
  • Increased trust from clinicians, patients, and regulators