Artificial Intelligence for Improving Patient and Medication Safety
This special session is organised and supported by the following educational partner
King Faisal University, Beirut Arab University and British American Tobacco
- Dr. Sulaf Assi, School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
- Dr Abdullah Al-Hamid, College of Clinical Pharmacy, King Faisal University, KSA.
- Miss Mahasin Khalil El Dimassi, Faculty of Engineering, Beirut Arab University, Beirut, Lebanon.
- Dr Touraj Ehtezazi, School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
- Dr Salwa Sami Yasen, NHS, UK.
- Dr Thomas Coombs, British American Tobacco, Southampton, UK.
Patient safety is a human rights and public health issue. It is defined as the freedom from unexpected harm to patients during the provision of healthcare. Harm can occur at any stage of the treatment process including disease diagnosis, prescription and medication dispensing stage, and can occur in primary, secondary or tertiary healthcare settings. Yet, treatment processes are often complicated, and this is attributed to the complexity of medical/clinical data that is often unstructured, unbalanced and contain missing information. Moreover, many factors contribute to the treatment process including, but not limited to, physiological-, sociodemographic-, clinical-, medicine-, healthcare system-, lifestyle- and/or behaviour-related factors. Artificial intelligence (AI), including machine learning, big data and neural networks, allows accurate predictions in healthcare based on historic data. Within patient and medication safety scenario, AI is able to offer accurate and transparent predictions in timely manner; hence, avoiding patient safety-related incidents. This session presents successful cases of the use of AI in the fields of patient and medication safety from medical/clinical data collection, inclusivity and analysis.
Therefore, this special session invites authors to submit high-quality research papers on emerging technology for medical/clinical application, covering topics which include (but are not limited to) the following:
• Medical and clinical datasets validity
• Developing AI models for predicting drug related problems
• Use of innovative approaches for detecting substandard and fake medicines
• Accurate diagnosis of diseases using AI algorithms
• Deep learning for medical image analysis
• Advanced Natural Language Processing models for understanding patients’ behaviour, beliefs and attitude.
• Equality, diversity and inclusion in healthcare settings
• 3-D printing of medicines and medical devices
• Automation in healthcare settings