AI IN Healthcare And Diagnosis Full Syllabus
Duration: 8–10 Weeks | Level: Beginner to Intermediate
Designed For: Medical students, tech enthusiasts, healthcare professionals, AI beginners
Goal: Learn how Artificial Intelligence is reshaping diagnosis, treatment, and healthcare systems.
Module 1: Introduction to AI in Healthcare
- What is AI in simple words (with healthcare examples)
- How is AI used in hospitals, labs, and patient care?
- From stethoscope to smart systems: the evolution
- Opportunities & challenges in AI healthcare
Mini Task: List 5 ways AI is already helping doctors today.
Module 2: Basics of Artificial Intelligence (Non-Technical)
- What is AI, ML & Deep Learning – the simplest explanation
- Types of learning: supervised, unsupervised, reinforcement (only in medical context)
- Why data is “the fuel” for AI diagnosis
- Real-life examples: AI in COVID detection, cancer screening, etc.
Activity: Watch and analyze an AI tool demo (e.g., IBM Watson or Google Health)
Module 3: Medical Data & Imaging – AI’s Playground
- Types of healthcare data: Electronic Health Records (EHR), medical images, lab reports
- What is medical imaging? (X-ray, MRI, CT scan, Ultrasound)
- How AI “reads” medical images
- Role of AI in detecting tumors, fractures, strokes, pneumonia
Hands-on (No-code): Try a public demo like Chest X-ray AI diagnosis (Google or Stanford tools)
Module 4: Real-World AI Diagnosis Applications
- AI in cancer diagnosis (breast, lung, skin)
- AI in ophthalmology (detecting diabetic retinopathy)
- AI in cardiology (ECG reading, stroke prediction)
- AI in pathology: analyzing blood reports, biopsy slides
Case Study: DeepMind’s AI vs human radiologists in breast cancer detection
Module 5: Electronic Health Records (EHR) and Predictive Analytics
- How AI reads patient records
- Predictive modeling: how AI predicts diseases before symptoms show
- AI in hospital management & early warning systems
- NLP in healthcare: extracting insights from patient notes
Mini Project: Design a simple AI system use-case to reduce hospital wait time
Module 6: AI in Personalized Medicine & Treatment Planning
- What is precision medicine?
- How AI helps tailor treatments to individual patients
- Pharmacogenomics: AI in drug response prediction
- Real-world examples: IBM Watson for Oncology
Discussion: Can AI recommend better treatments than doctors?
Module 7: AI in Remote Care, Wearables & Patient Monitoring
- Role of AI in telemedicine
- AI in wearable devices (Fitbit, Apple Watch, ECG devices)
- Monitoring chronic diseases using AI
- AI-powered virtual health assistants & chatbots
Hands-on: Explore how an AI chatbot helps with symptom checking (e.g., Ada, Babylon)
Module 8: Ethics, Risks & Legal Issues in Medical AI
- Can AI make life-or-death decisions?
- Bias in medical data = unfair AI decisions
- Patient privacy & data protection (HIPAA, GDPR overview)
- Explainable AI (XAI) – doctors must trust AI output
- Who is responsible if AI makes a wrong diagnosis?
Debate: Should AI be allowed to make independent diagnosis?
Final Project Ideas (Choose One)
- Case study report: “How AI Diagnosed Disease X More Accurately Than Doctors”
- AI-based solution proposal for rural healthcare challenges
- Create a mockup EHR AI system flow (even on paper/Canva/Figma)
- Research poster: “The Future of AI in My Country’s Healthcare System”
Bonus Tools You May Explore
| Tool/Platform | Purpose |
|---|---|
| Google Health AI | Research & imaging diagnosis |
| IBM Watson Health | Oncology, decision support |
| Ada / Babylon / Your.MD | AI-based symptom checkers |
| Qure.ai | X-ray & CT scan diagnosis |
| PathAI | Pathology & biopsy analysis |
| Microsoft InnerEye | Medical imaging segmentation |
