Digital Health and Artificial Intelligence in Pharmacology

This session explores the transformative role of digital health technologies and artificial intelligence (AI) in modern pharmacology and toxicology, highlighting how biological, clinical, and patient-specific data can be leveraged to improve drug development, therapy optimization, and patient safety. The session will cover applications such as AI-driven drug discovery, predictive modeling of pharmacokinetics and pharmacodynamics, virtual screening for toxicity, and biomarker identification. Clinical decision support systems, wearable devices, mobile health applications, and electronic health records will be discussed as tools to monitor therapeutic response, adherence, and adverse events in real-time. Emphasis will be placed on integrating biological information—genomics, proteomics, metabolomics—with computational models to personalize therapy, predict drug interactions, and minimize side effects. Case studies will illustrate how machine learning algorithms can optimize dosing, identify patients at risk of adverse drug reactions, and improve clinical trial design and patient recruitment. Regulatory and ethical considerations, including data privacy, algorithm transparency, and bias mitigation, will also be addressed to ensure safe and responsible implementation. Participants will gain insight into how AI and digital health are reshaping pharmacological research, drug safety monitoring, and clinical practice, enabling more precise, efficient, and patient-centered therapeutic strategies. By combining biological understanding with advanced analytics, this session aims to equip participants with knowledge and practical approaches to harness digital and AI-driven innovations for improved healthcare outcomes.

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