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 in Healthcare surveys how machine learning is being applied across the whole of medicine, from the clinic to the back office. You explore the major application areas — medical image analysis for radiology and pathology, clinical risk and outcome prediction from electronic health records, diagnostic support, and operational analytics that improve hospital efficiency. Throughout, the course keeps a firm grip on what makes healthcare different: patient safety, fairness, privacy and the need for clinical validation. You finish with a broad, grounded understanding of where AI genuinely helps healthcare and how to apply it responsibly. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI across healthcare — medical imaging, clinical prediction, diagnostics, patient data and hospital operations — with the safety and ethics the domain demands.
📋 Course Objectives
1. Apply machine learning to medical imaging and diagnostics.
2. Build clinical prediction models from health records.
3. Use AI for operational and workflow analytics.
4. Address patient safety, privacy and fairness.
5. Understand the clinical validation of medical AI.
👥 Who Should Enroll?
• Healthcare and clinical professionals
• Health-data scientists and engineers
• Health-tech and hospital-analytics teams
• Students of health informatics
🚀 Key Learning Outcomes
• A broad, grounded view of AI across healthcare.
• A clinical-data or imaging project.
• A safety- and ethics-first 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
Day 1: Introduction to AI in Healthcare
Explore the definition and significance of AI in healthcare. • Identify core AI techniques such as ML, NLP, and computer vision. • Discuss data privacy, bias, and ethical considerations.
Day 2: Applications and Tools
Implement AI‑powered diagnostic models for medical imaging. • Build predictive analytics solutions for patient outcomes. • Utilize leading AI libraries and platforms (TensorFlow, Keras, PyTorch, IBM Watson Health, Google Health AI).
Day 3: Advanced Topics & Hands‑On Project
Apply AI to genomics for personalized medicine and drug discovery. • Develop a complete AI solution to predict patient readmission risk. • Explore emerging trends such as AI‑driven mental health, surgical robotics, and telemedicine.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | IBM Watson Health |
| Covered Tool / Platform | Google Health AI |
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