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Health AI Governance Advisory is an independent educational and advisory initiative focused on sociotechnical governance of AI in healthcare

By combining evidence-informed insights from health informatics, implementation science, and AI governance, this initiative supports more responsible, practical, and sustainable approaches to AI use in healthcare.

Beyond the Code: Building Trust in High-Risk Health AI

Advisory Areas

Technical Readiness Review

$0.00

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

All assessments are delivered through a socio-technical lens, considering data, models, people, workflows, and governance together.

Clinical Workflow Integration Review

$149.00

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

All assessments are delivered through a socio-technical lens, considering data, models, people, workflows, and governance together.

Regulatory Compliance Review

$0.00

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

All assessments are delivered through a socio-technical lens, considering data, models, people, workflows, and governance together.

Human-AI Interaction and Usability Review

$199.00

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

All assessments are delivered through a socio-technical lens, considering data, models, people, workflows, and governance together.

Fairness and Bias Review

$249.00

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

All assessments are delivered through a socio-technical lens, considering data, models, people, workflows, and governance together.

Workshops & Masterclasses

For Healthcare Providers

Clinician-oriented education focused on human–AI interaction, workflow integration, cognitive burden, trust, usability, oversight responsibilities, and the practical realities of using AI safely and effectively in contemporary clinical practice.

For Health Leaders

Executive-focused sessions examining organisational readiness, governance maturity, accountability structures, implementation strategy, generative AI adoption, and the sociotechnical challenges associated with operationalising AI in healthcare environments.

For Researchers

Evidence-informed workshops and masterclasses exploring implementation readiness, translational barriers, and the operational realities that influence whether healthcare AI can succeed beyond pilot and research settings.

The Pain Points I Help You Navigate

A horizontal infographic with icons and text about challenges in healthcare: trust, bias and inequity, non-compliance, workflow disruption, and clinical harm. Each section features a colored icon and a brief description.

About Me

Dr Robab Abdolkhani

I provide specialist expertise in Health AI governance, helping healthcare organisations safely integrate AI into clinical operations. Drawing on a PhD in Health Informatics and extensive research and industry experience in sociotechnical health AI systems, I bring a rigorous, evidence-based approach to assessing risk and operational readiness. Through governance framework design, data pipeline assurance, and comprehensive sociotechnical evaluation, I deliver practical oversight structures that ensure regulatory alignment, data integrity, and safe, transparent deployment. My advisory supports healthcare organisations to adopt AI confidently, responsibly, and at scale.

🎓 Associate Degree (Medical Record Administration)
🎓 BSc. Degree (Health Information Management)
🎓 MSc. Degree (Health IT)
🎓 PhD. Degree (Health Informatics and Information Systems)
🌐 The Global Agency for Responsible AI in Health
🌐 International Open Digital Health Organization
🌐 Australasian Institute of Digital Health
🎖️ ISO/IEC 42001 AI management systems training
🎖️AI governance and responsible AI training
🎖️ AI auditing and assurance training
🎖️ Cloud computing and applied AI learning pathways