Bachelor of Science in Artificial Intelligence in Healthcare AI and Bioinformatics

About the Programme

Eligibility

Minimum 50% marks in Class 12th in any stream

Students with a CUET / SAT (DI Code - 71057 ) score will be given preference in the merit list

NOTE-Specializations: - Option to pursue with Research (Research Project in Final Year) or without Research.

For Migration from Other Universities:
1) B.Sc AI In Healthcare AI and Bioinformatics- Ist yr. minimum 50% marks or 5.0 CGPA on a 10 point scale
2) XII with minimum 50% Marks
Note - Option to pursue with Research (Research Project in Final Year) or without Research.

Fee Structure

Duration

4 Years

Tuition Fee (per annum)

1,10,000

Exam Fee (per annum)

20,000

Medical Insurance (Per Annum)

650

Caution Money (One Time Payment )

2000

Student ID Card

1000

Program Structure

Programme Educational Objectives

Holistic Knowledge and Lifelong Learning: Graduates will possess a solid foundation in Artificial Intelligence principles, data science, and computational techniques, enabling them to adapt to emerging technologies and continue lifelong learning in both professional and personal pursuits.

Multidisciplinary and Industry-Relevant Skills: Graduates will integrate knowledge across disciplines, combining computer science, mathematics, statistics, and domain-specific knowledge, to solve complex real-world problems and contribute effectively to the AI industry, research, and entrepreneurship.

Ethical and Social Responsibility: Graduates will demonstrate ethical behaviour, cultural sensitivity, and social responsibility while applying AI solutions, ensuring that their work positively impacts society and upholds the values of inclusivity and sustainability.

Leadership and Innovation: Graduates will develop strong communication, collaboration, and leadership skills, enabling them to contribute innovatively to the AI ecosystem, foster startups, and lead diverse teams in dynamic global work environments.

Programme Specific Outcomes (PSOs)

Apply AI Techniques and Tools: Develop and implement AI solutions using techniques such as machine learning, deep learning, natural language processing, and computer vision, utilizing modern programming languages and software tools.

Data-Driven Decision Making: Analyze, interpret, and visualize complex datasets using statistical and computational methods to derive actionable insights for informed decision-making across various domains.

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