Monday, August 31, 2026

2:30 PM - 5:00 PM

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Details

Organised by the Chinese Pharmaceutical Association (CPA) Programme: Prof. Yu Zhang, Huazhong University of Science and Technology, China and Mr Paul Sinclair, FIP President, Australia Introduction: Hospital pharmacists are evolving from traditional drug dispensers into clinical integrators. The FIP 2026 plan highlights the use of predictive analytics to identify at-risk patients, enabling personalised pharmacist interventions. In China, this aligns with recent NMPA guidelines that emphasise pharmacist-led validation and prohibit full automation of prescription review. Chinese hospitals are already piloting AI systems for high-risk drug monitoring and adverse reaction prediction, reinforcing the need for a core competency framework that blends clinical knowledge with data interpretation skills. As generative AI rapidly enters Chinese pharmacy education, the FIP agenda addresses critical challenges such as algorithm transparency and dynamic curriculum design. Chinese institutions are developing adaptive learning platforms featuring virtual patients and immersive laboratories, but face pressing issues of AI literacy among both students and practicing pharmacists. The FIP discussions on explainable algorithms and ethical AI use directly reflect regulatory demands in China for transparent, auditable educational tools—preparing future pharmacists to work confidently alongside intelligent systems. The technical track emphasises “human-in-the-loop” frameworks for ethical governance of generative AI in pharmacotherapy recommendations, alongside machine learning models to predict ADRs from drug combinations. China’s resource disparities are addressed in Panel 1 on equitable AI transition, highlighting infrastructure and data access gaps. Panel 2 focuses on regulatory readiness, calling for validation benchmarks to move AI tools from research to clinical approval. Panel 3 explores emerging roles like AI triage pharmacist and clinical AI auditor, ensuring that AI augments rather than displaces clinical judgment—a principle strongly echoed in recent Chinese policies on AI and healthcare workforce transformation. Programme: Learning objectives: 1. Analyze the evolving role of hospital pharmacists – from traditional dispensing to clinical integration – and evaluate how AI‑driven predictive analytics supports personalized pharmaceutical interventions, while respecting regulatory boundaries. 2. Identify key challenges and solutions in implementing generative AI and machine learning in pharmacy practice and education, including algorithm transparency, curriculum adaptation, AI literacy, and ethical “human‑in‑the‑loop” governance for pharmacotherapy recommendations. 3. Assess the practical pathways for equitable AI transition, regulatory validation, and emerging professional roles (e.g., AI triage pharmacist, clinical AI auditor), and understand how these align with current Chinese health policies on workforce transformation. Take home messages: 1. AI augments, not replaces, clinical judgment. The “human‑in‑the‑loop” principle is central to Chinese policy and FIP discussions – pharmacists must remain the decision‑makers, with AI serving as a supportive tool for risk identification and monitoring. 2. Education must catch up with technology. Adaptive learning platforms with virtual patients and immersive labs are emerging, but systematic AI literacy training for both students and practicing pharmacists is urgently needed, alongside transparent, auditable educational tools to meet regulatory standards. 3. Bridging the resource gap is essential for equitable AI adoption. Infrastructure and data access disparities across Chinese hospitals must be addressed through tailored validation benchmarks and phased implementation strategies, ensuring that AI benefits are distributed fairly and do not widen existing inequalities in pharmaceutical care.