AI in Clinical Decision Support (Classroom, Synchronous and Asynchronous e-learning)
About This Course
This course explores how Artificial Intelligence (AI) enhances healthcare decision-making. Learners will study the role of AI agents, digital twins, and simulations in clinical settings. The course teaches how to design and develop decision support systems using knowledge-based, expert, and rule-based approaches. Advanced topics include Bayesian networks, probabilistic models, and causal inference, focusing on improving decision-making processes. By mastering these techniques, learners will gain valuable skills for building AI-driven clinical support systems and advancing healthcare innovation. This course prepares learners for impactful careers in AI-powered healthcare solutions and clinical decision support development.
What You'll Learn
• Explain the roles of AI agents, digital twins, and simulations in clinical decision support, and evaluate their impact on healthcare outcomes.
• Design and build clinical decision support systems, using various methodologies such as knowledge-based, expert, and rule-based systems.
• Apply techniques like Bayesian networks, probabilistic models, and causal inferences to optimise healthcare decision-making through data-driven strategies.
Entry Requirements
The programme is primarily for clinicians and healthcare professionals. Professionals, such as scientists and researchers, interested in how to apply AI technologies to biomedical data are also welcome. There is no pre-requisite to enroll in the programme but some background in the healthcare sector and/or computer literacy would be an advantage.