Programme Overview
A specialist three-day programme for physicians, healthcare administrators, researchers and health-policy leaders. It covers the full spectrum of AI in healthcare, from clinical decision support, medical imaging and diagnosis to hospital operations, population health and medical research. Given the high stakes and heavy regulation, substantial time is devoted to patient safety, bias, clinical validation and regulatory alignment — including Ministry of Health (MoH) requirements, Saudi Food and Drug Authority (SFDA) implications where relevant, the PDPL for health data, and SDAIA’s AI Ethics Principles in the clinical and health context. The hands-on work uses anonymised clinical and operational data.
Learning Objectives
By the end of this programme, participants will be able to:
- Describe the state of AI in clinical practice, medical research and healthcare operations.
- Use AI tools for clinical documentation, literature summarisation, patient communication and administrative tasks.
- Evaluate clinical AI systems — including medical imaging, diagnostic support and risk-stratification tools — for validity, bias and fit.
- Apply AI to healthcare operations, including scheduling, workflow optimisation and population-health analytics.
- Critically interpret AI-generated clinical and research output, including uncertainty, bias and failure modes.
- Apply MoH, SFDA, PDPL and SDAIA requirements to AI use in clinical and health settings.
- Lead the conversation on responsible AI adoption with clinicians, IT, administration and patients.
Programme Content & Modules
Day 1: The healthcare AI landscape
How AI is reshaping healthcare globally and in the Kingdom. Clinical, administrative, research and population-health applications. Where AI has matured, where it is emerging, and where it has failed. The Saudi health-transformation context, including the Health Sector Transformation Program and the move to a model-of-care approach.
AI in medical imaging across radiology, pathology, ophthalmology and cardiology. Clinical decision-support systems. Risk stratification and early warning. Evidence-quality and validation expectations. Case studies of clinical AI systems deployed in the Kingdom and in international health systems.
Scheduling, workflow optimisation, the revenue cycle, clinical documentation and population-health analytics. How AI is reshaping the non-clinical side of the hospital and health system. Integration with HIS, EMR and operational platforms.
Day 2: AI tools in clinical and research practice
Practical work with AI tools for clinical-documentation support, literature summarisation, patient-education drafting and administrative tasks. Prompt engineering for clinical accuracy. Verification practices.
AI in literature review, research-proposal writing, research-design assistance and data analysis. AI-based population-health analytics. Genomic and multi-omics data considerations. Responsible research use of AI.
Combining AI output with clinical judgement. Handling AI recommendations when they diverge from clinical intuition. Communicating AI-supported recommendations to patients and colleagues. A group exercise on a realistic clinical scenario supported by AI.
Day 3: Safety, regulation and sovereignty
How clinical AI can fail. Bias, distribution shift, automation complacency and liability. Validation expectations for deployed clinical systems. Post-deployment monitoring.
Saudi MoH requirements for clinical and health-system AI. SFDA implications where relevant (Software as a Medical Device). The PDPL and health-data protection. SDAIA’s AI Ethics Principles in a healthcare context. Patient rights and consent considerations. Sector ethics-committee expectations.
The long-term strategic implications of relying on foreign AI infrastructure for clinical and health data. Where sovereign boundaries matter in healthcare. Applied capstone project: each participant presents a responsible-AI-adoption roadmap for a clinical, administrative or research use case in their organisation.
Suggested Duration
Three training days.
Target Audience & Prerequisites
Target audience: Physicians, nurses, allied-health professionals, clinical-informatics specialists, hospital administrators, health-system executives, medical researchers, public-health specialists and health-policy leaders in hospitals, primary care, health authorities, research institutions and the Ministry of Health.
Prerequisites: A valid clinical licence, an advanced nursing qualification, an allied-health qualification, or an equivalent professional role in healthcare, research or health policy. Basic computer literacy. No prior background in AI is required. A laptop is required for the hands-on sessions.
