Programme Parameters
Graduate / Intermediate
8 Weeks (80 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This specialized course is tailored for participants aiming to pioneer AI-driven innovations in the diagnostic and medical devices sector. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This specialized course is tailored for participants aiming to pioneer AI-driven innovations in the diagnostic and medical devices sector.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Where Software Becomes a Medical Device
• Software as a Medical Device: definitions, classification and intended use statements
• The intended-use boundary and how a claim changes the regulatory burden
• Combination products and AI embedded in hardware
Building Under a Quality System
• ISO 13485 design controls applied to a machine-learning workflow
• Requirements, traceability and design history for a learning system
• ISO 14971 risk management and hazard analysis for AI failure modes
Clinical and Analytical Validation
• Analytical validation versus clinical validation versus clinical utility
• Study design for a diagnostic claim, including reference standard selection
• Statistical planning: sample size, subgroup analysis and pre-specification
Managing a Model That Learns
• Locked versus adaptive algorithms and predetermined change control plans
• Version control, retraining triggers and documenting what changed
• Real-world performance monitoring and post-market surveillance duties
Access and Sustainability
• Regulatory pathways across FDA, EU MDR and CDSCO, and their divergence
• Cybersecurity requirements for connected diagnostic devices
• Reimbursement and the health-economic case a purchaser will demand
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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