Page 52 - 23rd Convocation Booklet

Curriculum Architecture: The 20-Module Blueprint

Designing a course for high-level medical professionals required a balance of technical rigor and

clinical relevance. The NBEMS AI programme is structured into 20 comprehensive modules
delivered over a six-month period. Crucially, the course is designed to be accessible to clinicians who

have no prior background in computer programming or technical mathematics.

Phase 1: Foundational Literacy and Data Philosophy

The initial modules (1-5) focus on demystifying the technology. Module 1 clarifies what AI is—and
what it is not—setting realistic expectations for its role in clinical settings. Module 2 builds a "gentle

bridge" from the familiar world of Biostatistics to modern AI concepts like Machine Learning and

Deep Learning. This is followed by training in data integrity, where clinicians learn that the quality
of AI output is directly linked to the quality of clinical questions and data input—the "Garbage In,

Confident Garbage Out" principle. Module 5 introduces the concept of "Prompt Thinking," teaching

doctors how to interact effectively with Large Language Models (LLMs) to extract meaningful
clinical value.

Phase 2: The Engine of AI – ML and Deep Learning

Modules 6 through 9 delve into the technical families of AI. Clinicians are introduced to Supervised

and Unsupervised Learning, understanding how algorithms identify patterns in medical imaging,
pathology slides, and genomic sequences. The focus remains on the clinical utility of these models—

understanding their sensitivity, specificity, and the bias-variance trade-offs that can affect diagnostic

accuracy.

Phase 3: Clinical Deployment and Outcome-Action Pairing

The core of the programme (Modules 10-13) focuses on the "at-the-bedside" application. These
sessions explore how to integrate AI tools into the electronic health record (EHR) workflow and how

to pair AI-driven outcomes with clinical actions. This ensures that the AI is not just a statistical
curiosity but a tool that improves patient-centered decision-making.

Phase 4: Ethics, Law, and Cybersecurity

In Modules 14 through 18, the curriculum addresses the significant ethical and legal challenges of
AI. These include:

• Algorithmic Bias: Ensuring that AI tools do not perpetuate racial or socioeconomic disparities
in healthcare.

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