Chair
Prof. Yu Zhang, China
Introduction
Artificial Intelligence (AI) is revolutionizing clinical pharmacy by tackling systemic challenges, such as, advancing precision medicine, adverse reaction screening through-out database and enhancing patient safety. Currently, AI assistant technology is being extensively utilized in the medical field. For instance, the AI tablet reading technology developed by China's DAMO Academy and Alibaba Cloud Computing, as well as Wuhan Union Medical College Hospital's reliance on the analysis of medical databases to predict drug dosages and potential adverse reactions, thereby guiding clinical pharmacological decision-making.
However, it cannot be ignored that patients can also use these artificial intelligence tools to participate in the decision-making process of drug treatment, thereby gaining a deeper understanding of their own disease conditions and drug information. After obtaining relevant information, patients may improve their treatment compliance due to a full understanding of the diagnosis and treatment plan and achieve more efficient communication with medical staff. However, there is also the possibility that patients may refuse to adopt professional advice due to the inconsistency between the results of the decision-making system and professional opinions.
Future advancements are expected to focus on precision medicine, where AI-driven pharmaco-genomic tools—such as those analyzed in cost-utility studies for clinical decision support—could tailor drug therapies to individual genetic profiles, reducing adverse events and optimizing outcomes. Additionally, AI could enhance real-time monitoring of disease progression, as seen in studies linking immune biomarkers (e.g., CD4 counts, cytokines levels) to clinical outcomes, enabling dynamic adjustments to therapies. Key challenges remain, including data standardization across diverse populations, algorithmic bias due to non-representative datasets, and regulatory hurdles in validating AI tools for clinical use. Bridging these gaps will require interdisciplinary collaboration, robust ethical frameworks, and investments in AI infrastructure to ensure scalability and equity in global healthcare systems.
Programme:
| 12:45 – 12:50 | Opening & Welcome speech |
| Prof. Yu Zhang, China | |
| Paul Sinclair, President FIP, Australia | |
| 12:50 – 13:10 | Introduction of Intelligent Pharmacy Service |
| Prof. Rongsheng Zhao, China | |
| 13:10 – 13:30 | AI and the general use in pharmacy |
| Prof. Lars-Åke_Söderlund, Sweden | |
| 13:30 – 13:50 | AI Enhanced Pharmaceutical Education |
| TBC | |
| 13:50 – 14:15 | Discussion & Sharing |
Learning objectives:
1. What are intelligent pharmacy services? How should adapted AI to pharmacy service?
2. How can patients achieve a balance between professional medical opinions and AI-generated recommendations when there is a discrepancy?
Take home messages:
AI-assisted decision-making should be a part of medication safety, but it cannot replace the opinions of professional medical personnel.
