Programme Parameters
Graduate / Intermediate
3 Days (4.5 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 for Healthcare Applications takes an application-first tour of how machine learning is used across medicine today. You work through the concrete use cases — image-based diagnosis, clinical risk prediction, drug discovery, virtual assistants, and operational optimisation — understanding for each what problem it solves, what data it needs, and how mature it is. Rather than deep methodology, the emphasis is a clear, well-organised map of real applications and their value and limits. You finish able to recognise and evaluate AI applications across healthcare. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course surveys AI healthcare applications — a practical catalogue of how machine learning is used across diagnosis, imaging, records, drug discovery and operations.
📋 Course Objectives
1. Catalogue AI use cases across healthcare.
2. Understand the data each application needs.
3. Assess the maturity and value of each.
4. Recognise limits and risks per use case.
5. Evaluate an application for a health setting.
👥 Who Should Enroll?
• Healthcare and clinical professionals
• Health-tech and product teams
• Administrators and decision-makers
• Students of medical AI
🚀 Key Learning Outcomes
• A practical map of healthcare AI applications.
• The ability to evaluate use cases.
• An application-first perspective.
• 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
Healthcare Data and Its Realities
• EHR structure, coding systems and the messiness of real clinical data
• FHIR and interoperability standards for extracting usable datasets
• Confounding by indication and other traps in observational health data
Clinical Risk Modelling
• Deterioration, readmission and sepsis prediction as canonical problems
• Calibration over discrimination: why AUC alone misleads clinicians
• Prospective versus retrospective evaluation and label timing
Clinical Text and Documentation
• Information extraction from notes, discharge summaries and referrals
• De-identification and its residual re-identification risk
• Ambient documentation tools and the verification burden they shift
Integration Into Care
• Workflow integration and alert design that avoids fatigue
• Silent deployment and shadow evaluation before clinical influence
• Monitoring for drift as case mix and practice patterns change
Regulation, Safety and Equity
• Software as a medical device: regulatory classification and evidence
• Clinical safety cases and post-market surveillance duties
• Equity auditing across demographic groups and access conditions
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 |
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