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The initiative evaluates how AI tools interact with clinical workflows, regulatory expectations, organisational accountability structures, and data practices across healthcare settings. 

We move beyond simple statistical checks to evaluate your entire data pipeline for hidden biases, contextual integrity, collection incentives, and sociotechnical risks

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

Advisory Areas

The Pain Points I Solve

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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 leaders to adopt AI confidently, responsibly, and at scale.

  • 🎖️ ISO/IEC 42001 Certification in AI Management System

    🎖️ Certified Lead Auditor ISO 42001 AI

    🎖️ AI Governance Professional (AIGP) Certification

  • 🎓 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

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Why work with me?

🏥 I understand healthcare systems, not just algorithms

👁️ I evaluate real-world use, not just model accuracy

🌍 I align with global AI regulations

📄 I write professional-level reports for executives, regulators, and academic environments

👥 I understand the cultural, organisational, and human impacts of technology in healthcare

💡 I simplify complex AI issues for non-technical healthcare executives

🛡️ I bring a lens of equity and patient safety, not just fairness metrics

Projects

A detailed infographic illustrating core principles for healthcare data management, including data accuracy, accessibility, completeness, consistency, interpretability, relevancy, and timeliness, each accompanied by relevant icons and brief descriptions.

A sociotechnical framework for data quality management in AI-enabled health wearables

Internationally validated via expert consensus

Blog