B.E Artificial Intelligence and Machine Learning AI for Healthcare Syllabus - Mumbai University 2026
This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.
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Syllabus for AI for Healthcare
Module 1: Introduction
- 1.1 Overview of AI , ML and DL ,A Multifaceted Discipline, Applications of AI in Healthcare Prediction, Diagnosis, personalized treatment and behavior modification, drug discovery, followup care etc,
- 1.2 Realizing potential of AI in healthcare, Healthcare Data - Use Cases.
Module 2: AI, ML, Deep Learning and Data Mining Methods for Healthcare
- 2.1 Knowledge discovery and Data Mining, ML, Multi classifier Decision Fusion, Ensemble Learning, Meta-Learning and other Abstract Methods.
- 2.2 Evolutionary Algorithms, Illustrative Medical Application-Multiagent Infectious Disease Propagation and Outbreak Prediction, Automated Amblyopia Screening System etc.
- 2.3 Computational Intelligence Techniques, Deep Learning, Unsupervised learning, dimensionality reduction algorithms.
Module 3: Evaluating learning for Intelligence
- 3.1 Model development and workflow, evaluation metrics, Parameters and Hyperparameters, Hyperparameter tuning algorithms, multivariate testing, Ethics of Intelligence.
Module 4: Natural Language Processing in Healthcare
- 4.1 NLP tasks in Medicine, Low-level NLP components, High level NLP components, NLP Methods.
- 4.2 Clinical NLP resources and Tools, NLP Applications in Healthcare. Model Interpretability using Explainable AI for NLP applications.
Module 5: Intelligent personal Health Record
- 5.1 Introduction, Guided Search for Disease Information, Recommending SCA's. Recommending HHP's , Continuous User Monitoring.
Module 6: Future of Healthcare using AI 08
- 6.1 Evidence based medicine, Personalized Medicine, Connected Medicine, Digital Health and Therapeutics, Conversational AI, Virtual and Augmented Reality, Blockchain for verifying supply chain, patient record access, Robot - Assisted Surgery, Smart Hospitals, Case Studies on use of AI and ML for Disease Risk Diagnosis from patient data, Augmented reality applications for Junior doctors.
- 6.2 Blockchain for verifying supply chain, patient record access, Robot - Assisted Surgery, Smart Hospitals, Case Studies on use of AI and ML for Disease Risk Diagnosis from patient data, Augmented reality applications for Junior doctors. Total 39
Textbooks
- 1 Arjun Panesar, "Machine Learning and AI for Healthcare”, A Press.
- 2 Arvin Agah, "Medical applications of Artificial Systems ", CRC Press
- 1 Erik R. Ranschaert Sergey Morozov Paul R. Algra, “Artificial Intelligence in medical Imaging- Opportunities, Applications and Risks”, Springer
- 2 Sergio Consoli Diego Reforgiato Recupero Milan Petković,“Data Science for Healthcare-Methodologies and Applications”, Springer
- 3 Dac-Nhuong Le, Chung Van Le, Jolanda G. Tromp, Gia Nhu Nguyen, “Emerging technologies for health and medicine”, Wiley.
- 4 Ton J. Cleophas • Aeilko H. Zwinderman, “Machine Learning in Medicine- Complete Overview”, Springer
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.