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Course Detail

Learning Objectives

  • LO1: To provide basic introduction to artificial intelligence and its role in biomedicine and healthcare
  • LO2: To introduce different concepts, methods, and potential intelligent systems in medicine

Course Outcomes

  • CO1: Ability to understand decision support systems
  • CO2: Ability to apply neural networks and deep neural networks for healthcare problems
  • CO3: Ability to apply time-series forecasting for healthcare applications

Course Contents

Introduction of concepts, methods, and potential of intelligent systems in medicine: History and status quo, and decision support system. Application on any specific area of interest, Risk stratification, Data acquisition and pre-processing, Feature identification and extraction, Model selection and implementation, Model validation and evaluation with performance metrics, visualization and interpretability. Introduction to neural networks and applications in healthcare. Deep neural networks, Convolutional neural networks, ARIMA for time series forecasting, SHAP analysis for feature analysis and selection


  1. Begg, Rezaul, Daniel TH Lai, and Marimuthu Palaniswami, computational intelligence in biomedical engineering. CRC Press, 2007.
  2. Hudson, Donna L., and Maurice E. Cohen. Neural networks and artificial intelligence for biomedical engineering, Institute of Electrical and Electronics Engineers, 2000.
  3. Agah, Arvin, Introduction to medical applications of artificial intelligence, Medical Applications of Artificial Intelligence, CRC Press, 2013. 18-25.

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