Abstract
AI is becoming an integral part of medicine, enhancing patient care by optimizing processes and increasing accuracy, ultimately leading to improved healthcare outcomes. Machine learning assists in analyzing radiological images, pathology slides, and electronic medical records (EMRs), complementing clinicians’ expertise in diagnosis and treatment. Effective AI management requires strong communication, leadership, coordination, and oversight in a rapidly evolving field that leverages human intelligence to address complex decision-making challenges. As a multidisciplinary domain, artificial intelligence has evolved at the intersection of computer science, cybernetics, information theory, psychology, linguistics, and neurophysiology While AI is on the rise, many people are still less familiar with its concepts. This comprehensive overview from Axis 1 aims to demystify AI for policymakers, opinion leaders, and the public, highlighting its growing influence and the social, economic, and governance challenges it presents. While most current AI research focuses on specific scientific and engineering challenges, often distinct from cognitive science, the concept of general AI has long served as a guiding vision. To advance AI in therapeutic science, initiatives such as the Therapeutics Data Commons are helping to access and evaluate AI performance across a variety of therapeutic approaches and discovery stages. Developing safe and effective medicines is essential to meeting the healthcare needs of billions of people around the world. Deep learning algorithms, a subset of machine learning, focus on understanding multiple layers of distributed representations