Skip to Main Content

Artificial Intelligence in Health Care - (AIH-601) (HND): Course Content

Artificial Intelligence (AI) in healthcare refers to the use of AI technologies like machine learning, deep learning, and natural language processing to improve patient outcomes, enhance healthcare operations, and reduce costs.

Course Outline

COURSE CONTENTS
1. Introduction to Artificial Intelligence in Healthcare
• Definition and scope of AI in healthcare, focusing relevant healthcare sciences.
• Historical perspective and milestones in AI in Healthcare research
• Applications of AI in clinical practice and healthcare research
2. Fundamentals of Machine Learning
• Supervised, unsupervised, and reinforcement learning
• Classification methods and model evaluation
• Bias-variance tradeoff and model interpretability
3. Artificial Neural Networks
• Deep Learning Frameworks
• Neural Networks Architectures, e.g., Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• Transfer Learning and Pre-trained Models4. Programming in Python for Healthcare
• Introduction to Python Programming
• Essential Python Libraries for Healthcare (NumPy, SciPy, Pandas etc)
• Advanced Python Libraries for Healthcare Applications (TensorFlow, Keras, BioPython
etc)
5. Big Data Challenges
• Data Quality and Management
• Data Integration and Scalability
• Data Storage and Feature Engineering
• Dimension Reduction Techniques
6. AI in Healthcare and Imaging
• Image classification, preprocessing, and segmentation
• Radiomics and quantitative imaging biomarkers
• Applications of AI software’s in all relevant health sciences.
7. AI in Diagnostics and Disease Prediction
• Predictive modeling for disease risk assessment
• Diagnostic decision support systems
• Early detection of diseases using AI algorithms
• Practical Applications and Case studies
8. Natural Language Processing (NLP) in Healthcare
• Text mining and information extraction from clinical notes and prescriptions
• Clinical language understanding and medical coding
• Applications of NLP in electronic health records (EHR) analysis and clinical documentation
9. AI in Personalized and Treatment Planning
• Pharmacogenomics and precision
• Treatment recommendation systems
• Practical Applications and Case Studies
10. Ethical, Legal, and Social Implications (ELSI) of AI
• Bias and fairness in AI algorithms
• Privacy and security of healthcare data
• Regulation and policy considerations for AI in healthcare.

Course Description and Learning Outcomes

COURSE DESCRIPTION
This course provides an introductory exploration into the application of artificial intelligence (AI) in the field of Allied health sciences. Students will delve into fundamental AI concepts, focusing on the use of AI algorithms and technologies for enhancing diagnostic processes. This course will help students learn advance understanding of how AI and machine learning techniques are transforming healthcare delivery, clinical decision-making, and research. Students will gain
foundational knowledge to implement AI techniques such as machine learning and deep learning in healthcare research and clinical practices.
Course Learning Outcomes
By the end of this, students will be able to
Learning Domain
Cognitive Affective Psychomotor
1. Describe the basics of artificial intelligence in allied health C2  A1 P1
2. Understand the basic principles and terminology of artificial intelligence and machine learning. C2 A1 P1
3. Understand Natural language processing and medical coding C2 A2 P1
4. Describe the use of AI in diagnostics, medical imaging, disease prediction, and treatment planning. C3 A2 P1
5. Understand and apply the basic AI software tools in practice. C3 A3 P2
6. Learn ethical, legal, and social implications of AI in health C2 A2 P1

Recommended Books