Programme Parameters
Graduate / Intermediate
3 Weeks (30 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
AI in Diagnostic & Medical Devices focuses on the discipline of putting machine learning inside regulated medical products. You learn how AI powers modern diagnostic and monitoring devices, and — crucially — what it takes to do it safely: designing for reliability, validating clinically, and navigating the regulatory pathways (such as FDA and EU MDR) that govern AI-enabled devices. The course covers the full product lifecycle, from data and design to post-market monitoring of learning systems. You finish able to reason about developing an AI-enabled medical device. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in diagnostic and medical devices — embedding machine learning in devices, and the design, validation and regulation of AI-enabled medical products.
📋 Course Objectives
1. Explain how AI powers diagnostic devices.
2. Design AI devices for reliability and safety.
3. Validate device AI to a clinical standard.
4. Navigate medical-device regulatory pathways.
5. Plan post-market monitoring of AI.
👥 Who Should Enroll?
• Medical-device engineers and developers
• Regulatory and quality professionals
• Health-tech and diagnostics teams
• Students of medical technology
🚀 Key Learning Outcomes
• An understanding of AI in medical devices.
• A regulation- and safety-first perspective.
• A product-lifecycle approach.
• 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
AI in Diagnostics and Medical Devices
Introduction to AI in Diagnostics • Workflow of AI-Enabled Devices • Adoption Drivers and Common Pitfalls
Data and Signal Fundamentals
Types of Data in Diagnostic Systems • Data Quality and Preprocessing • Ground Truth and Reference Standards
Model Approaches for Diagnostic Tasks
Detection and Classification • Segmentation and Measurement Support • Anomaly Detection and Fault Monitoring
Evaluation and Performance Reporting
Core Performance Metrics • Calibration and Thresholds • Contextual Performance Reporting
Clinical Validation and Deployment Readiness
Validation Strategy • Workflow Integration • Building Trust in Deployment
Safety, Reliability, and Risk Management
Failure Modes and Safe Design • Alarm Management and Reliability • Incident and Corrective Action Planning
Post-Deployment Monitoring
Drift and Data Shift • Ongoing Performance Oversight • Controlled Updates and Change Management
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | SPSS |
| Covered Tool / Platform | DICOM Viewers |
| Covered Tool / Platform | EHR Systems |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PubMed |
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