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Course Detail

Course Name AI in Diagnostics
Course Code 25CLG541
Program M. Sc. Clinical Genomics
Semester 2
Credits 3
Campus Kochi

Syllabus

Unit 1

Introduction to AI in Healthcare and Diagnostics, Overview of AI and ML in healthcare, Evolution of diagnostic technologies, Types of AI systems in medical diagnostics, Benefits, limitations, and challenges, Overview of regulatory landscape

Unit 2

Machine Learning Fundamentals for Diagnostics, Supervised, unsupervised, and reinforcement learning, Data preprocessing and feature extraction, Model selection and evaluation metrics, Common algorithms: Decision Trees, SVM, Random Forest, k-NN, Neural Networks

Unit 3

Image Analysis and Pattern Recognition, Role of AI in medical imaging (X-rays, MRIs, CT scans), Deep learning for image classification and segmentation (CNNs, U-Nets, Tensorflow, Pytorch), Use cases in radiology and pathology, Hands-on example: Tumor detection in medical scans

Unit 4

Predictive Analytics and Decision Support, Predictive modeling using patient records, Risk stratification and early disease detection, AI-based clinical decision support systems (CDSS), Integration with EHRs (Electronic Health Records)

Unit 5

AI in Genomics and Laboratory Diagnostics, Introduction to bioinformatics and genomics data, ML for variant calling, gene expression profiling, AI applications in lab test data interpretation, Use cases in personalized medicine and oncology

Unit 6

Case Studies, Ethics, and Future Directions, Real-world case studies across different diagnostic fields, Explainable AI (XAI) in healthcare, Ethical, legal, and privacy considerations, Challenges in clinical adoption and the road ahead.

Introduction

Credits: 3 (45 classes)

Preamble

AI in Diagnostics introduces students to the growing role of artificial intelligence and machine learning in modern diagnostic systems. The course covers algorithms used for image analysis, pattern recognition, predictive analytics, and decision support in medical diagnostics. Real-world case studies will highlight AI integration in pathology, oncology, radiology, genomics, and lab data interpretation.

Objectives and Outcomes

Course outcomes

CO1: Explain the evolution, types, and role of AI in healthcare diagnostics.

CO2: Apply basic machine learning algorithms for healthcare data analysis.

CO3: Analyze medical images using AI tools like CNNs for diagnostic purposes.

CO4: Build predictive models for disease risk and decision support.

CO5: Interpret genomic and lab data using AI for personalized diagnostics.

CO6: Evaluate case studies and ethical issues in clinical AI applications.

Program outcome

PO1: Bioscience Knowledge

PO2: Problem Analysis

PO3: Design/Development of Solutions

PO4: Conduct Investigations of complex problems

PO5: Modern tools usage

PO6: Bioscientist and Society

PO7: Environment and Sustainability

PO8: Ethics

PO9: Individual & Team work

PO10: Communication

PO11: Project management & Finance

PO12: Lifelong learning

0 – No affinity; 1 – low affinity; 2 – Medium affinity; 3 – High affinity

CO–PO Mapping Table:

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

3

2

–

1

3

2

–

2

–

2

–

3

CO2

2

3

2

2

3

–

–

–

–

–

–

2

CO3

2

3

3

2

3

–

–

–

–

–

–

2

CO4

3

3

3

3

3

–

–

–

–

2

2

3

CO5

3

3

2

3

3

2

–

–

–

–

–

3

CO6

2

2

–

2

2

3

–

3

–

2

–

3

Program Specific Outcomes (PSO):

PSO1. Apply fundamental molecular biology principles to interpret clinical genomic data.
PSO2. Use molecular techniques (e.g., PCR, RT-PCR, sequencing) to detect genetic mutations and biomarkers.
PSO3. Analyze genotype-phenotype correlations in inherited and acquired disorders.
PSO4. Identify pathogenic variants from NGS data and interpret their clinical relevance.
PSO5. Correlate molecular pathways with disease mechanisms and therapeutic targets.
PSO6. Develop and validate diagnostic assays based on molecular biology principles.
PSO7. Utilize molecular biology to support pharmacogenomic profiling and therapy optimization.
PSO8. Integrate multi-omic data (genomic, transcriptomic, epigenomic) for personalized health solutions.
PSO9. Apply molecular knowledge to cancer genomics, infectious diseases, and rare genetic disorders.
PSO10. Translate molecular discoveries into clinical interventions through evidence-based practice.

0 – No affinity; 1 – low affinity; 2 – Medium affinity; 3 – High affinity

CO–PSO Mapping Table:

COs

PSO1

PSO2

PSO3

PSO4

PSO5

PSO6

PSO7

PSO8

PSO9

PSO10

CO1

2

–

–

–

–

–

–

2

–

–

CO2

2

–

–

–

–

–

–

2

–

–

CO3

2

–

–

–

–

–

–

–

–

–

CO4

2

–

–

2

–

–

–

2

–

2

CO5

3

–

2

3

–

–

3

3

–

3

CO6

–

–

–

–

–

–

–

–

–

2

Evaluation Pattern

Evaluation Pattern: 50+50 = 100

Internal Assessment – 50%
Periodical 1 Exam 20%
Periodical 2 Exam 20%
Continuous Assessment Assignment/Test/Quiz 10%
50%
End Semester Examination- 50%
Theory Exam 50%
50%
Total 100%

Textbook/ References

Textbook:

  • Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial Intelligence in Healthcare: Past, Present and Future. Stroke and Vascular Neurology. 2017;2(4):230–243.

Reference Book:

  • Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial Intelligence in Radiology. Nat Rev Cancer. 2018;18(8):500–510.

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