Graduate Diploma in AI for Healthcare
Program Overview
Graduate Diploma in AI for Healthcare
Hospital-Ready Training Program
Duration: 9 Months (3 + 3 + 3)
Program Fee: $2,700 (Phase 1: $900, Phase 2: $1,800)
Program Overview
The Post Graduate Diploma in AI for Healthcare is a comprehensive 9-month hospital-ready program designed for students and professionals who want to build careers in AI-driven healthcare, medical imaging, and precision medicine. The program follows a 3-phase model:
- Phase 1 – Preparatory Training for Projects (3 Months, Online): Foundations in AI, medical imaging, precision medicine, and healthcare data.
- Phase 2 – Project Training & Development in Greece (3 Months, On-Site): Applied projects in AI for imaging, genomics, and clinical workflows.
- Phase 3 – Project Internship & Submission (3 Months, Remote / India): Internship-style experience to finalize, document, and present projects.
This program emphasizes applied skills, hands-on project experience, and hospital-ready competencies to ensure graduates are fully prepared for careers in hospitals, diagnostics centers, and health-tech companies globally.
Program Objectives
By the end of the Post Graduate Diploma, students will be able to:
- Apply AI techniques to medical imaging and clinical datasets for diagnostics and decision support.
- Integrate precision medicine and genomic insights into AI-driven workflows.
- Develop and deploy hospital-ready AI projects with clinical relevance.
- Interpret and communicate AI model results for healthcare applications.
- Prepare professional portfolios demonstrating applied AI expertise.
- Secure hospital, health-tech, and remote AI opportunities in India and abroad.
Curriculum Structure
Phase 1 – Preparatory Training for Projects (3 Months | Online)
Courses focus on building strong foundations in:
- Healthcare Systems & Digital Transformation – Healthcare operations, digital workflows, hospital data management.
- Python Programming for Healthcare AI – Core programming, data handling, and AI libraries for healthcare applications.
- Machine Learning Fundamentals – Supervised & unsupervised learning, classification, regression.
- Deep Learning & Neural Networks – CNNs, RNNs, and advanced architectures for imaging and clinical data.
- NLP for Clinical Text – Extraction, interpretation, and predictive analytics on EMR/EHR datasets.
- Medical Imaging & Diagnostics AI – AI for radiology, imaging pipelines, and diagnostic support.
- Precision Medicine & Genomics – Cancer genomics, biomarkers, pharmacogenomics, and patient stratification.
- Deployment, Ethics & Regulations – AI deployment, GDPR/EU compliance, clinical safety, and ethics.
Phase 2 – Project Training & Development in Greece (3 Months | On-Site)
Students apply their learning to hands-on, guided projects:
- Advanced Medical Imaging AI – Multi-modal imaging, radiology workflow optimization, AI model testing.
- Precision Medicine & Genomics – Multi-omics integration, patient stratification, AI-driven treatment planning.
- Project Development – Design, develop, and implement AI projects under mentor guidance, ready for submission.
Students work in teams to develop hospital-ready solutions with real-world clinical relevance, preparing them for Phase 3.
Phase 3 – Project Internship & Submission (3 Months | Remote / India)
The final phase focuses on applied, in-house internship experience:
- Project Finalization: Enhance AI models and clinical pipelines from Phase 2.
- Deployment & Validation: Test solutions with hospital-ready standards and datasets.
- Portfolio Development: Document outcomes, methodologies, and professional skills.
- Presentation & Review: Submit projects for evaluation, simulating hospital/industry project delivery.
- Career Mentoring: Guidance on CVs, interviews, and placements in hospitals, health-tech, and remote AI opportunities.
Graduates emerge as hospital-ready AI professionals with tangible projects, applied skills, and career guidance, making them highly employable in radiology departments, diagnostics centers, hospitals, and health-tech startups, with readiness for global opportunities.
Learning Approach
- Hands-on applied training with real and simulated datasets
- Project-based learning for portfolio development
- Mentorship from healthcare AI experts
- Emphasis on hospital workflows, regulatory standards, and ethics
Who Should Enroll
- Graduates and professionals in life sciences, healthcare, IT, or related fields
- Candidates aspiring for hospital, diagnostics, or health-tech AI roles
- Learners seeking international exposure and career-ready applied skills
- Students aiming to develop AI projects for clinical impact
Professional Scope
Graduates can pursue roles in:
- Medical Imaging AI Specialist
- Clinical Data Analyst
- AI & Healthcare Analytics Professional
- Digital Health Coordinator
- Precision Medicine Analyst
Employment Settings: Hospitals, diagnostics centers, health-tech startups, remote AI healthcare projects, and global healthcare companies.
20 Reasons to enroll into the program
- Hospital-Ready Training: Gain practical skills directly applicable to radiology departments, diagnostics, and clinical workflows.
- AI in Healthcare Expertise: Learn cutting-edge AI tools, deep learning, and NLP for medical data analysis.
- Precision Medicine Skills: Work with genomics, biomarkers, and patient stratification to support personalized treatment.
- Medical Imaging Mastery: Hands-on exposure to X-ray, CT, MRI, and multi-modal imaging AI applications.
- Project-Based Learning: Develop real-world projects during training to showcase your skills.
- International Exposure: Three months of on-site project training in Greece.
- Applied AI Portfolio: Graduate with a professional portfolio of hospital-ready AI projects.
- Career Guidance: Mentorship on interviews, CVs, and job applications for India and global roles.
- High Employability: Designed to meet hospital, health-tech, and diagnostics industry needs.
- Global Opportunities: Prepare for EU, remote, and Indian healthcare AI positions.
- Hands-on Project Internship: Phase 3 ensures applied experience in realistic hospital-like settings.
- Cutting-Edge Curriculum: Covers Machine Learning, Deep Learning, NLP, Medical Imaging AI, and Genomics.
- Ethics & Regulations: Training includes AI deployment, healthcare compliance, and ethical considerations.
- Multi-Disciplinary Approach: Integrates AI, genomics, clinical workflows, and hospital operations.
- Small Cohorts: Personalized attention with mentorship and guidance from industry experts.
- Translational Skills: Learn to apply AI solutions in practical healthcare settings, not just theory.
- International Standards: Exposure to EU medical regulations and hospital-ready protocols.
- Networking Opportunities: Connect with mentors, faculty, and international peers.
- Remote & Flexible Learning: Phase 1 and Phase 3 are online and accessible from India.
- Career Pathway Readiness: Graduate fully equipped for roles in radiology, diagnostics, AI healthcare startups, and hospitals worldwide.
Graduate Diploma in AI for Healthcare
AI-Driven Variant Analysis & Risk Assessment
AI-Driven Variant Analysis & Risk Assessment
AI-Driven Variant Analysis & Risk Assessment
What you will learn
- Understand how genetic variants are identified and interpreted in medical genomics
- Apply AI-assisted approaches to prioritize clinically and biologically relevant variants
- Distinguish between germline and somatic variants in disease contexts
- Perform risk assessment based on genomic and phenotypic information
- Understand principles of genetic risk modeling and uncertainty
- Interpret variant significance using evidence-based criteria
- Recognize limitations of AI models in genomic interpretation
- Apply ethical considerations in AI-based variant analysis, including bias and responsible use
Multi-modal Biomarker Integration
AI-Driven Variant Analysis & Risk Assessment
AI-Driven Variant Analysis & Risk Assessment
What you will learn
- Understand different types of biomedical data used in modern genomics research
- Work conceptually with genomic, transcriptomic, proteomic, imaging, and clinical data
- Integrate multiple data types to improve disease characterization and stratification
- Apply AI-based methods for biomarker discovery
- Correlate biomarkers with disease features and outcomes
- Use data visualization techniques to explore and communicate multi-modal data
- Understand principles of biomarker validation and reproducibility
- Interpret integrated biomarker results in a research and translational context
Precision Medicine Informatics
Pharmacogenomics & Treatment Optimization
Pharmacogenomics & Treatment Optimization
What you will learn
- Understand the foundations of precision medicine and personalized care
- Use genomic and clinical data for patient stratification and disease subtyping
- Apply AI-based methods to predict disease progression and outcomes
- Understand how multi-omics data informs treatment decisions
- Learn concepts behind clinical decision-support systems
- Interpret predictive models and their outputs responsibly
- Communicate precision medicine insights clearly to scientific audiences
- Recognize ethical and data-governance considerations in precision medicine
Pharmacogenomics & Treatment Optimization
Pharmacogenomics & Treatment Optimization
Pharmacogenomics & Treatment Optimization
What you will learn
- Understand how genetic variation influences drug response
- Analyze drug–gene interactions relevant to treatment outcomes
- Apply AI-based models to predict treatment response and toxicity
- Understand principles of therapy optimization and stratification
- Learn how pharmacogenomics supports precision dosing and safety
- Interpret treatment-related genomic data in a research setting
- Appreciate regulatory and ethical considerations in genomics-guided therapy
- Integrate pharmacogenomic insights into broader precision medicine frameworks
