M.Sc. AI in Public Health
This Course Structure is Curated as per the NEP-2020 Guidelines
Course Overview
The M.Sc. AI in Public Health program at Malla Reddy Vishwavidyapeeth, Hyderabad, is a postgraduate course designed to integrate artificial intelligence (AI) with public health and financial sectors. This interdisciplinary program aims to equip students with the skills necessary to apply AI techniques for solving complex problems in public health management, financial services, and economic planning.
This program focuses on equipping students with the theoretical knowledge and hands-on skills from 1st semester itself. The curriculum covers AI algorithms, machine learning, big data analytics, and their applications in healthcare and finance. Students learn to analyze large datasets, predict healthcare trends, optimize financial strategies, and design AI-driven solutions to enhance decision-making processes in both sectors. Key areas of focus include AI in epidemiology, healthcare financing, insurance, financial risk management, and policy development.
Graduates of this program are Skilled in applying artificial intelligence to analyze healthcare data and financial systems, improving decision-making, resource allocation, and policy development. Career opportunities include roles as AI Public Health Analysts, Health Data Scientists, Financial Health Analysts, AI Healthcare Consultants, and Policy Advisors in public health organizations, financial institutions, healthcare technology firms, and governmental agencies. Enabling them to pursue further higher studies & do research in AI – Public Health along with plenty of job opportunities globally.
Course Details
Description: 2 Years Degree Program
No. of Credits: 80 minimum and as specified
Academic Qualification: A Bachelor’s degree in B.Sc. Pharmacy, B.Sc. Life Sciences, BDS, MBBS, or equivalent with at least 50% aggregate marks from a recognized university.
Semester | Name of the Subject |
Semester 1 | Introduction to AI in Public Health, Fundamentals of Health Economics, Epidemiological Data Analysis, AI Tools and Techniques, Research Methodology and Biostatistics, Practical: Data Preprocessing and AI Model Basics |
Semester 2 | Advanced AI for Disease Surveillance, Financial Modeling in Healthcare, Health Policy and Decision-Making, Machine Learning Applications in Public Health, Ethical AI Practices, Practical: Building Predictive Models |
Semester 3 | Natural Language Processing (NLP) in Health Systems, Blockchain in Health Finance, Public Health Informatics, Internship/Industry Exposure in AI Applications, Practical: Advanced Health and Finance Analytics |
Semester 4 | AI-Driven Innovations in Global Health, Risk Assessment and Management, Entrepreneurship in Health and Finance Technology, Thesis/Research Project |
- AI for Disease Surveillance and Prevention: Proficiency in using AI technologies for early detection, monitoring, and prevention of diseases in public health settings.
- Health Data Analytics: Skills in processing and analyzing large-scale health datasets to identify patterns, trends, and actionable insights for public health interventions.
- Predictive Modeling in Public Health: Expertise in developing and applying predictive models for outbreak forecasting, resource allocation, and risk management.
- AI-Driven Policy Making: Knowledge of integrating AI tools to assist policymakers in designing and implementing data-driven health policies.
- Ethical AI in Public Health: Understanding of ethical considerations, including equity, bias mitigation, and patient data privacy, in AI applications.
- Integration of AI with Public Health Systems: Ability to design and implement AI solutions compatible with existing health infrastructure for improved service delivery.
- Digital Health Ecosystem Development: Competence in creating AI-powered platforms for health education, behavior change communication, and community health improvement.
- Certification in AI for Disease Surveillance: Specialized training in using AI technologies to monitor and predict public health threats.
- Data Science in Public Health Certification: Focus on using data science tools to analyze and address public health challenges.
- Ethical AI in Healthcare Certification: Advanced training on the ethical use of AI in public health decision-making and interventions.
- Predictive Analytics in Public Health Certification: Skills in building and deploying predictive models for outbreak forecasting and health risk assessments.
- AI Policy and Governance Certification: Understanding the role of AI in shaping public health policies and systems.
- Ph.D. in AI and Public Health: Research-focused study in AI applications for addressing global and regional health challenges.
- Postgraduate Diploma in Health Informatics: Specialized study in informatics and AI for managing public health data and systems.
- M.Sc. in Global Health with AI Focus: Advanced studies integrating AI tools for tackling global public health issues.
- M.B.A. in Health Technology Management: Training in managing AI-driven health projects and technology systems for public health improvements.
Duration | Roles and Responsibilities | Salary Range |
0-3 years | Public Health Data Scientist, AI Health Analyst, Disease Surveillance Specialist | ₹6,00,000 – ₹9,00,000 per annum |
3-5 years | Senior AI Consultant in Public Health, Health Data Scientist, Policy Advisor for AI Implementation | ₹9,00,000 – ₹14,00,000 per annum |
5-10 years | AI Public Health Project Manager, Health Informatics Specialist, Director of AI in Public Health Systems | ₹14,00,000 – ₹22,00,000 per annum |
10+ years | Chief AI Public Health Officer, Global Health AI Consultant, Director of Public Health Innovation | ₹22,00,000+ per annum |
Fee Structure Per Academic Year
| Tuition Fee | Miscellaneous Fee | Scholarship | ||
| 125000 ₹ | 10000 ₹ | Above 90% – 25000 ₹ | Between 80-90% – 12500 ₹ | Between 70-80% – 6250 ₹ |
