Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This program emphasizes the ethical considerations of using AI in healthcare, such as fairness, bias, accountability, and patient privacy. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This program emphasizes the ethical considerations of using AI in healthcare, such as fairness, bias, accountability, and patient privacy.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A demonstrable AI Enablement 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
Principles Applied to Clinical AI
• Autonomy, beneficence, non-maleficence and justice as operational constraints
• Informed consent when a model contributes to a clinical decision
• Accountability when responsibility is distributed across developer, vendor and clinician
Equity in Clinical Models
• Sources of bias: sampling, label, measurement and deployment
• Documented cases where clinical algorithms disadvantaged patient groups
• Fairness metrics, their incompatibility, and choosing among them defensibly
Methods and Their Honest Limits
• SHAP, LIME and attention maps: what they do and do not establish
• Saliency methods that look convincing while being unreliable
• Inherently interpretable models as an alternative to post-hoc explanation
Trust, Reliance and Automation Bias
• Calibrated trust: appropriate reliance rather than maximum acceptance
• Automation bias and deskilling risk in routine use
• Designing explanations that support rather than replace clinical reasoning
Regulation and Institutional Practice
• Transparency and explainability requirements in emerging regulation
• Ethics committee review and institutional deployment governance
• Post-deployment monitoring for equity as well as accuracy
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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