AI in Healthcare
AI in Healthcare
A 6-month, fully online postgraduate program that fuses core machine learning with hands-on applied healthcare AI — built and taught by practitioners who have shipped real clinical models, run real deployments, and navigated real regulatory review.
This program page reflects our latest curriculum draft. Fees, faculty, and career-support details shown below are the ones on file in our admissions system — figures like guarantees or hiring-partner counts mentioned in course descriptions are illustrative and being finalized. Contact admissions for current details.
Why Healthcare AI, Why Now?
Hospitals and health-tech companies are racing to deploy AI for diagnosis, triage, and operations — but most teams lack people who understand both machine learning and the realities of clinical data, privacy, and regulation.
- •55% of health systems say AI deployment skills are critical to their 2026 roadmap
- •40% faster diagnostic triage reported by teams using validated clinical AI models
- •65% of health-tech job postings now name FHIR/HL7 or clinical-data fluency
- •3.4x more interview callbacks for candidates with a shipped clinical AI project
What Employers Are Actually Screening For
- •Clinically-literate ML engineers
- •AI-augmented diagnostic support
- •Cross-functional health-tech translators
- •People who validate and deploy, not just prototype
The Gap Is Real
How AI is changing what different health-tech and clinical roles actually do day to day.
- •Health-Tech Data Scientist — Pre-AI: Trained models on public benchmark datasets disconnected from real clinical workflows. Post-AI: Builds and validates models directly on de-identified clinical data with governance checks built in.
- •Clinical Informatics Analyst — Pre-AI: Manually reconciled EHR fields across incompatible hospital systems. Post-AI: Uses FHIR-standardized pipelines to integrate and analyze EHR data reliably.
- •Medical Imaging Engineer — Pre-AI: Reviewed scans one at a time with no automated triage support. Post-AI: Deploys computer-vision triage models that flag high-priority scans in real time.
- •Health-Tech Product Manager — Pre-AI: Wrote AI feature specs without understanding model validation or regulatory constraints. Post-AI: Scopes AI features with clear validation plans and regulatory pathways from day one.
- •Hospital Operations Analyst — Pre-AI: Forecast bed capacity and staffing with static spreadsheets updated weekly. Post-AI: Runs live ML-driven demand forecasts that update as patient flow changes.
Why Medivex AI?
- •Domain + AI, Together: Every module — imaging, EHR data, clinical NLP — is taught with AI embedded from day one, so you learn to apply machine learning to real clinical questions, not bolt it on afterward.
- •Taught by Practitioners: You're taught by practitioners who have shipped AI models into real clinical or health-tech products, not just people who've published benchmarks.
- •Hands-On, No Theory Theatrics: You clean clinical data, train models, validate performance, and prototype products — delivering outputs that look like real deployable systems.
- •Real Datasets, Real Problems: You work with real, messy healthcare datasets — imaging, EHR, claims — modeled on real hospital and health-tech systems.
- •Built-In Regulatory Fluency: Every capstone requires a validation and governance write-up, so you graduate already speaking the language hospital review boards expect.
- •Cohort-Based Accountability: Weekly live sessions and peer project reviews keep you shipping on schedule instead of stalling out on a self-paced course.
Traditional vs. Medivex AI
- •AI & Domain — Traditional: Taught as separate electives. Medivex AI: Embedded in every module.
- •Assessment — Traditional: Essays & written exams. Medivex AI: Shipped models & live capstones.
- •Format — Traditional: Fixed campus schedule. Medivex AI: 100% online, fits your job.
- •Cohort Size — Traditional: 200+ students, one-size-fits-all. Medivex AI: Small live cohorts with direct faculty access (~1:12).
- •Tooling — Traditional: Legacy academic software. Medivex AI: Production tools — TensorFlow, MONAI, FHIR/HL7, NVIDIA Clara.
AI Strategy for Health Systems — Phase 3 of 5
Identify high-impact AI opportunities, design deployment strategy, and lead real-world transformation in a clinical setting — from the first prototype to the boardroom pitch.
- •Model Validation & Deployment: Establish rigorous validation and monitoring standards for clinical AI models.
- •AI Governance & Regulatory Pathways: Navigate FDA/CE pathways, model cards, and hospital governance committees.
- •Health-Tech Strategy: Evaluate build-vs-buy-vs-partner decisions for AI initiatives inside health systems.
- •Change Management in Clinical Settings: Drive clinician adoption of AI tools without disrupting care workflows.
- •Vendor & Partner Evaluation: Assess third-party AI vendors against clinical safety and interoperability criteria.
- •Skills covered: Model validation, Clinical deployment, AI governance, Regulatory pathways, Stakeholder buy-in, Vendor evaluation
How We Pick Our People
- •Shortlist Call: A quick conversation to understand your goals and alignment with the program.
- •Aptitude Test: A brief assessment of your domain thinking and analytical sharpness.
- •Interview: A deeper evaluation of your ambition, intent, and readiness to grow.
- •Offer Rollout: If the fit is clear, we extend an invitation to join the cohort.
Program Details
- •Format: 6 months, 100% online, live cohorts, sessions recorded for later
- •Cohort size: Small live cohorts, roughly a 1:12 faculty-to-learner ratio
- •Eligibility: Bachelor's degree in any discipline; open to graduates and professionals in engineering, data science, or clinical practice
- •Admissions: Shortlist call → Aptitude test → Interview → Offer rollout
- •Career support: AI-powered mock interviews, resume & profile workshops, 1:1 career strategy calls, curated peer community, job-search accountability pods
- •Certification: Verifiable digital certificate and shareable credential for LinkedIn and resume upon completion
Curriculum Outline
ML & Healthcare Data Foundations
6 MonthsMachine learning fundamentals, healthcare data standards (FHIR, HL7, DICOM), and data privacy — the core toolkit every healthcare AI practitioner needs. Note: this duration label reflects the overall program framing; the week-by-week breakdown covers the first 12 weeks of Phase 1.
Mini Capstone — Cross-System Integration
1 MonthClean a clinical dataset, train a baseline model, and present validated results the way a health-tech product review actually works.
AI Strategy for Health Systems
Identify high-impact AI opportunities, design deployment strategy, and lead real-world transformation in a clinical setting.
Specialisation Track (choose one)
Clinical AI & Predictive Diagnostics / Medical Imaging & Computer Vision / Health-Tech Product & AI Strategy.
Final Capstone
5 WeeksDesign and build an AI-powered healthcare solution end-to-end, presented to an industry panel.
Week-by-Week Breakdown
Set up your toolkit and get comfortable with Pandas, NumPy, and Jupyter on sample health datasets.
Linear & logistic regression on structured clinical data; intro to evaluation metrics.
Tree-based models and ensembling for diagnostic risk prediction.
Clustering and dimensionality reduction for patient segmentation.
Parsing and transforming real FHIR resources; interoperability standards.
Reading, viewing, and preprocessing medical imaging data.
Safe-harbor de-identification, consent, and responsible data handling.
Cleaning messy, multi-source EHR extracts into model-ready tables.
Sensitivity, specificity, calibration, and subgroup performance.
Build a first-pass readmission risk model end-to-end.
Iterate, validate, and document your model for review.
Consolidate learning and prep for the Mini Capstone.
Specialisation Tracks
Clinical AI & Predictive Diagnostics
Build models that support diagnosis and risk prediction from structured clinical data. Skills covered: Risk modeling, EHR data, Model validation, Clinical NLP.
Medical Imaging & Computer Vision
Apply deep learning to radiology, pathology, and other imaging modalities. Skills covered: CNNs, Segmentation, DICOM pipelines, Triage models.
Health-Tech Product & AI Strategy
Lead AI product decisions inside hospitals and health-tech companies. Skills covered: Product scoping, Governance, Build vs. buy, Stakeholder pitching.
Tools You'll Use
Capstone Projects
Mini Capstone — Cross-System Integration (Phase 2 of 5)
A 1-month capstone applying everything end-to-end: clean a clinical dataset, train a baseline model, and present validated results. Components include guided real-world projects (a readmission-risk model, an imaging triage classifier, or a clinical NLP note-summarizer), model validation design that holds up under clinical and regulatory scrutiny, and product-ready reporting with clear performance metrics and deployment considerations. Project options include building a 30-day readmission risk model from EHR data, training an imaging triage classifier on a chest X-ray dataset, prototyping a clinical note summarization tool, and presenting a one-page model card to a mock governance panel.
Final Capstone (Phase 5 of 5)
Design and build an AI-powered healthcare solution end-to-end over 5 weeks, solving a live clinical or health-tech problem with measurable, validated impact, presented to an industry panel. Components include end-to-end model development (from raw clinical data to a deployable prototype), clinical validation with rigorous bias checks, a deployment & monitoring plan, and stakeholder communication of findings and deployment plans in the format a hospital board would expect. Timeline: Weeks 1-2 scope & data prep, Week 3 model build, Week 4 validation, Week 5 panel presentation.
Program Faculty
Dr. Sneha Kulkarni
Lead Faculty, AI in Healthcare
Dr. Sneha Kulkarni brings 11+ years of industry and teaching experience in Machine Learning for Diagnostics.
Career Opportunities
What You'll Get
- ✓AI-powered mock interviews
- ✓Resume & profile workshops
- ✓Executive presence sessions
- ✓Mentorship from experienced practitioners
- ✓Curated peer community
- ✓Industry mixers & health-tech connects
- ✓1:1 career strategy calls
- ✓Job-search accountability pods
- ✓Ability to build and validate clinical AI models
- ✓Ability to apply ML to imaging & EHR data
- ✓Ability to communicate findings with clarity
- ✓Ability to navigate AI governance & regulation
- ✓Ability to bridge clinical and engineering teams
- ✓Ability to present deployment-ready reports
- ✓Ability to design monitoring plans post-deployment
- ✓Career pathways: Clinical AI Engineer, Health-Tech Data Scientist, Medical Imaging AI Specialist, Clinical Informatics Analyst, AI Product Manager (Health), Health-Tech Founder
Who This Is For
- •Engineering & Data Science Graduates: CS, data science, or engineering graduates wanting to specialize in applied healthcare AI.
- •Working Clinicians & Health-Tech Professionals: Clinicians, health-tech PMs, and analysts looking to add AI/ML fluency to their clinical toolkit.
- •Eligibility: Open to graduates and professionals with a background in engineering, data science, clinical practice, or a related field. A bachelor's degree in any discipline is required.
- •What we look for beyond the resume: Ambition, Ownership mindset, Curiosity about AI, Consistency under pressure, Coachability
Frequently Asked Questions
Success Stories
Hear from our alumni who have successfully transitioned into rewarding Medivex AI careers.
Ready to start AI in Healthcare?
Talk to admissions or download the full brochure to see the complete curriculum.