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BSCS - Specialization in AI in Medicine
Program Overview
BSCS (Specialization in AI in Medicine) at GU TECH, Al Ghazali University prepares students to lead the future of healthcare innovation.
This program blends Computer Science, Artificial Intelligence, Genomics, and Medical Data Science to solve real-world healthcare challenges.
Ideal for FSc Pre-Medical students who want to enter health-tech, precision medicine, computational biology, and AI-driven diagnostics.
Eligibility:
FSc Pre-Medical
Why Choose This Program?
Learn how AI is reshaping medical imaging, genomics, disease prediction, and clinical decision support.
Designed with experts in AI in Medicine, Computational Genomics, and Health Informatics.
Practice in modern computing and AI labs using real biomedical datasets.
Build a strong skillset for careers in medicine, biotechnology, healthcare analytics, and software engineering.
What You Will Learn
The program covers key domains in modern health technologies:
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Medical Imaging AI
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Predictive Modeling for Disease & Treatment
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Genomics & Precision Oncology
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Bioinformatics & Computational Genomics
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Machine Learning & Deep Learning
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Health Informatics Systems
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Data Science for Healthcare
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Software Engineering Fundamentals
Career Opportunities
Graduates can work in:
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Computational Genomics / Bioinformatics
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AI for Medical Diagnostics
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Pharmaceutical & Biomedical R&D
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Public Health Data Analytics
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Hospital Information Systems
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Health-Tech Software Development
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Research Labs & Precision Medicine Projects
You will be prepared for emerging roles in AI-driven healthcare, one of the fastest-growing global fields.
Why GU TECH?
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Expert faculty in AI in Medicine & Computational Biology
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Smart classrooms and high-tech computing labs
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Research opportunities with healthcare and AI-focused faculty
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Supportive environment with mentorship and career guidance
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Active societies, sports, and student engagement
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A modern campus designed for learning and innovation
Elective Courses for BSCS (AI in Medicine)
1. Medical Image Analysis
Learn AI and deep learning methods for detecting diseases from X-rays, CT scans, and MRI images.
2. Computational Genomics
Explore algorithms and AI tools used to analyze DNA, RNA, and mutation data in precision medicine.
3. Bioinformatics Algorithms
Study sequence alignment, gene prediction, and protein modeling techniques used in modern biology.
4. Machine Learning for Healthcare
Apply ML models to clinical datasets for diagnosis, risk prediction, and treatment outcomes.
5. Precision Oncology Informatics
Understand how genomic and clinical data guide targeted cancer therapies.
6. Clinical Decision Support Systems
Design AI-powered systems that assist doctors in diagnosis, treatment planning, and monitoring.
7. Health Data Analytics
Analyze hospital, clinical, and public health datasets to uncover trends and improve healthcare delivery.
8. Natural Language Processing in Medicine
Use NLP models on medical text, clinical notes, and biomedical literature for automated insights.
9. Biomedical Signal Processing
Study analysis of ECG, EEG, and other physiological signals using AI and signal models.
10. AI Ethics & Safety in Healthcare
Examine fairness, trust, privacy, and regulatory concerns in medical AI applications.
11. Computational Drug Discovery
Use AI and simulations to predict drug–target interactions and identify new therapeutic compounds.
12. Medical Robotics & Automation
Learn how robotics and AI are transforming surgeries, rehabilitation, and healthcare automation.
13. Public Health Informatics
Use data systems and analytical tools to support disease surveillance and population health.
14. Biomedical Databases & Knowledge Graphs
Understand medical ontologies, knowledge graphs, and data integration frameworks like SNOMED CT.
15. Deep Learning for Biomedical Research
Apply CNNs, RNNs, and Transformers to complex biological and clinical datasets.