Empowering CSE graduates to engineer the future of digital healthcare & AI innovation.

I-PRISM training to build a global, future-ready tech career in health & life sciences.

Your Learning Journey at a Glance

What you may have learned:

➤ Core programming, algorithms, OS, databases, AI/ML basics, software engineering, and systems development.

What you may not know:

➤ How clinical data, biosignals, Ayurveda-based VPK fingerprints, and multi-omics integrate into medical AI systems.

What you will learn:

➤ To build health-tech platforms, VPK-aware AI models, and integrative diagnostics powering the PRISM Health OS.

Basic Certification in PRISM

The PRISM Basic Certification is tailored for engineers and technologists, giving them biomedical and healthcare literacy from a systems perspective. Engineers are introduced to human biology, anatomy, physiology, and major medical conditions while learning how Vata-Pitta-Kapha concepts model homeostasis and system behaviour — offering new inspiration for innovation. They gain hands-on exposure to bio-signals, health datasets, and AI-based diagnostics such as digital pulse analysis and integrative telehealth platforms. The program enables engineers to speak both medical and technical language confidently, making them effective collaborators in emerging health-tech environments.

Who should join: ECE, EEE, CS/AI/ML, Mechatronics, Mechanical, Civil engineers — final-year students or early-career professionals interested in biomedical devices, digital health, or healthcare system innovation.

Outcomes
Apply engineering skills to healthcare innovation.
Work effectively with clinicians and biomedical teams.
Use PRISM digital tools and bio-signal analytics in real contexts.

Advanced Certification in PRISM

The Advanced Certification elevates engineers from skilled learners to innovators. Participants work on real-world healthtech solutions such as AI-based Nadi Pariksha devices, rehabilitation technologies, VR training tools, clinical data platforms, or decision-support systems embedding traditional medical knowledge. The curriculum features computational modelling, multi-omic data analytics, and entrepreneurial deployment strategies — including regulatory pathways and clinical readiness for medical devices and software. Engineers graduate with a prototype/portfolio and the capability to lead cross-disciplinary innovation in medtech.

Who should join: Students who completed the Basic program and who are already familiar with biomedical concepts who want to specialize in AI-driven healthcare, wearable/medical device engineering, robotics for therapy, or startup-led health innovation..

Outcomes
Become industry-ready healthtech innovators.
Build deployable prototypes and product concepts.
Use computational and systems-medicine models in design.

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Academic & Clinical Disciplines Covered in I-PRISM

CS/AI students learn the fundamentals of human anatomy, physiology, and pathology necessary to build credible healthtech. They study how diseases manifest across systems (e.g. how diabetes affects cardiovascular, nervous systems) and how traditional diagnostic methods (Ayurvedic pulse diagnosis, TCM tongue analysis) might correlate with biomedical signals. This equips them to design software that can integrate multi-dimensional diagnostic data.

Engineers gain a high-level understanding of Ayurvedic doshas, TCM meridians, and other traditional concepts, not to practice them clinically but to model them in software. For example, they learn what it means for a patient to be “Pitta” or “Yang-deficient” and how these profiles could be represented as data points or algorithmic rules in a system. Data-driven dosha mapping is a key objective – using large datasets to find patterns (e.g. linking symptom clusters to dosha types)[16].

This module helps technologists appreciate patient lifestyle factors. They explore how stress, exercise, diet, and meditation influence health outcomes and data readings. Learning objectives include designing user interfaces or apps that track these lifestyle inputs and incorporating mind-body metrics (like sleep quality, step count, mood) into predictive health models.

Engineers are introduced to bioinformatics and computational biology. They learn to handle genomic sequences, lab test datasets, and other biomarker data. For instance, a project might involve analyzing gene expression data to identify molecular signatures of an Ayurvedic herb’s effect. By understanding multi-omic biomarkers, they can better program AI diagnostic tools that account for a patient’s unique biological makeup.

Though not chemists, CS/AI students learn about databases of phytochemicals and natural products. They might write algorithms to scan herbal compound libraries for molecules that target specific proteins (similar to drug discovery). A learning objective is developing a simple AI that suggests herbal recommendations based on a patient’s conditions and the known molecular actions of plant compounds (integrating traditional formulary knowledge with cheminformatics).

This introduces cutting-edge biomedical tech (stem cells, tissue engineering, nanotech for drug delivery). Engineers learn where computational tools are needed – e.g. image processing for tissue scaffolds, or AI to predict stem cell differentiation outcomes. Their objective is to find roles for AI/ML in accelerating regenerative therapies, perhaps using simulations to model organ regeneration or to personalize regenerative protocols.

Building on their strength, this module goes deep into AI algorithms for medical data. They learn to develop and validate ML models for tasks like medical image analysis (X-rays, MRIs), predictive analytics for disease risk, or NLP for processing clinical notes. Uniquely, they are challenged to incorporate integrative data – e.g. creating a model that takes both EHR data and Ayurveda assessment inputs to predict patient outcomes. AI-based integrative diagnostics is a centerpiece: developing algorithms that fuse traditional and modern diagnostic criteria to improve accuracy[16].

CS/AI students learn to work with real-time data from biomedical sensors and wearables. Objectives include signal cleaning and feature extraction from ECGs, EEGs, or wearable fitness trackers. They also study communication protocols for medical IoT – ensuring that devices (glucose monitors, smart yoga mats, etc.) reliably transmit data to databases or apps. By mastering biosensor analytics, they can create systems that monitor patients remotely and alert clinicians or adjust treatments automatically[16].

This module merges systems biology with engineering. Students use computer models to simulate complex biological networks (e.g. metabolic pathways, immune system behavior). A learning task might be modeling how a combination of herbal and pharmaceutical treatments interacts in the body. This systems approach, including computational physiology, trains them to foresee multi-system effects of interventions[16].

Engineers learn how medical research is conducted – from clinical trial design to statistical analysis – so they can align their innovations with clinical evidence. They also address data privacy, security, and ethics in healthtech. A key objective is understanding regulatory standards (HIPAA, FDA software guidelines) to ensure that their AI diagnostic or health OS software can be safely deployed in real-world clinical settings.

In collaboration with medical experts, CS/AI students learn how treatment protocols are formulated and where decision-support tools are needed. For example, they might build a clinical decision system that helps doctors by suggesting integrative treatment options (like diet changes or acupuncture referrals) alongside prescriptions. They learn to incorporate outcomes data and patient preference into algorithmic treatment recommendations.

The culmination is a capstone where engineers actually build a prototype “Health Operating System” or similar product. This could be an AI-powered platform that aggregates a patient’s medical history, genomics, and lifestyle/dosha profile to provide personalized health insights. Or a telemedicine app that integrates with wearables and provides integrative health coaching. The goal is for each student (or team) to create a tangible tech solution demonstrating their ability to fuse computer science with integrative medicine.

Application Process

Stage – 1

Eligibility & Application

Applicants provide GATE score (preferred) or strong CGPA with portfolio of relevant tech projects, along with CV, SOP, and academic transcripts.

Stage – 2

Score Normalization

Academic Index is computed by standardizing graduation marks and entrance score (if available) to ensure fair merit evaluation.

Stage – 3

ISAT Examination

Engineering-specific ISAT section evaluates branch fundamentals (e.g., DSA, circuits, signals, mechanics) and ability to apply technology to healthcare.

stage – 4

Shortlisting

Shortlisting is based on CPIS ranking, balancing academic performance with analytical and technical aptitude.

stage – 5

Interview

Panel assesses innovation mindset, practical problem-solving, portfolio quality, and readiness to translate engineering into health-tech solutions.

stage – 6

Final Selection

Final Selection Score combines academic merit, ISAT percentile, and interview evaluation to determine admission offers.

stage -7

Enrollment & Bridging

Selected candidates complete bridging modules in human biology, anatomy, and physiology to prepare for healthcare-focused coursework.

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