AI for Healthcare Professionals — Menmozhi Technologies

Curriculum

Detailed 12-Week Learning Path

Structured in three progressive months — from Python fundamentals to deploying autonomous AI agents in clinical settings.

Month 1 — Python & Machine Learning for Healthcare

W1 Introduction to AI in Healthcare

AI vs ML vs Deep Learning vs GenAI

Current AI landscape in medicine

Clinical Decision Support Systems (CDSS)

Electronic Health Records & Medical Informatics

Healthcare data types: structured & unstructured

HIPAA, GDPR & Medical Data Privacy

AI Ethics & Bias in Healthcare

Regulatory frameworks: FDA, CE for AI devices

AI in Radiology, Pathology & Cardiology — overview

ROI of AI adoption in hospitals

W2 Python for Healthcare

Python environment setup (Jupyter, Colab, VS Code)

Data types, control flow & functions

OOP for healthcare data models

Patient data processing with lists & dicts

File I/O: CSV & JSON medical records

EHR parsing: HL7 & FHIR data formats

Calling healthcare APIs (OpenFDA, SNOMED)

Error handling in clinical pipelines

Python regular expressions for clinical text

Mini-project: Patient data ingestion pipeline

W3 Healthcare Data Analytics

NumPy for numerical medical data

Pandas: EHR tabular data manipulation

Data cleaning: missing values & outliers in labs

Hospital KPI dashboards (bed occupancy, LOS)

Disease prevalence & incidence analysis

Matplotlib & Seaborn for clinical charts

Plotly interactive clinical dashboards

Statistical testing in clinical trials

Survival analysis basics (Kaplan-Meier)

Project: Hospital operational analytics report

W4 Machine Learning for Healthcare

Supervised vs unsupervised learning

Classification: Logistic Regression, SVM, Random Forest

Disease diagnosis prediction models

Regression: Length-of-stay, lab value prediction

Hospital readmission prediction

Disease progression modelling

Feature engineering from EHR data

Model evaluation: AUC-ROC, sensitivity, specificity

Class imbalance in rare disease datasets

XGBoost & ensemble methods for clinical risk

Month 2 — Deep Learning & NLP

W5 Deep Learning Fundamentals

Artificial neural networks from scratch

Backpropagation & gradient descent

TensorFlow & Keras for medical models

Activation functions in clinical context

Regularisation: Dropout, BatchNorm

Transfer learning from pre-trained models

Model explainability: Grad-CAM, SHAP

Clinical AI evaluation: sensitivity vs specificity trade-off

GPU setup for medical AI training

Saving, versioning & deploying clinical models

W6 Medical Imaging & Computer Vision

Convolutional Neural Networks (CNN) deep dive

DICOM format: reading & preprocessing

PACS integration & HL7 image workflows

Chest X-ray classification (NIH ChestX-ray14)

CT scan slice segmentation

Brain MRI tumour classification

Data augmentation for medical images

ResNet, EfficientNet for radiology

U-Net for medical image segmentation

Model deployment with DICOM viewer integration

W7 NLP for Healthcare

Clinical text preprocessing & tokenisation

Named Entity Recognition for medical terms

ICD-10 / ICD-11 code extraction from notes

Medical abbreviation disambiguation

Clinical note summarisation (SOAP format)

Discharge summary auto-generation

Research paper mining & evidence extraction

Sentiment analysis of patient feedback

Prescription parsing & drug-interaction detection

De-identification of PHI from clinical text

W8 Medical Chatbots & Transformers

Transformer architecture for medical NLP

BERT, BioBERT & ClinicalBERT fine-tuning

PubMedBERT for biomedical literature

Medical Q&A system construction

Patient symptom checker chatbots

Medication adherence reminder bots

Intent detection for clinical dialogues

Multilingual support for regional health services

Safety, hallucination & guardrails in medical bots

Deploying chatbots via WhatsApp & web

Month 3 — Computer Vision, GenAI & AI Agents

W9 Advanced Computer Vision

OpenCV for medical image preprocessing

Image enhancement & noise reduction

Instance segmentation with Mask R-CNN

Medical image annotation tools (CVAT, LabelImg)

Skin lesion analysis & dermoscopy AI

Pathology whole-slide image processing

Video analysis for surgical procedure AI

3D medical image processing (volumetric CT)

Federated learning for multi-hospital imaging

Model optimisation for edge devices in clinics

W10 YOLO & Detection in Healthcare

YOLO v8/v10 architecture deep dive

Real-time tumour detection on scans

Retinal disease detection (DR, AMD, glaucoma)

Parasite egg & malaria detection from microscopy

Polyp detection in colonoscopy video

Surgical instrument tracking

Wound classification & monitoring

Building a custom medical detection dataset

Training & hyperparameter tuning for YOLO

Deploying detection models as REST APIs

W11 Generative AI for Healthcare

LLM fundamentals: GPT-4, Gemini, Claude

Prompt engineering for clinical accuracy

Clinical report generation & structured output

Medical documentation automation

Radiology report auto-drafting

Patient-facing health content generation

Medical education material creation

Fine-tuning LLMs on clinical corpora

Synthetic data generation for rare diseases

Responsible AI: hallucination detection & mitigation

W12 RAG Systems & AI Agents

Retrieval-Augmented Generation (RAG) architecture

LangChain & LlamaIndex for healthcare

Vector databases: ChromaDB, Pinecone

Medical knowledge base RAG system

Clinical guidelines Q&A (NICE, WHO, AHA)

Autonomous clinical decision agents

Multi-agent orchestration in hospital workflows

Healthcare workflow automation (scheduling, billing)

Agent safety: containment & human-in-the-loop

Capstone integration: end-to-end AI healthcare system

Capstone

Build Real Clinical AI Systems

Every project is production-oriented — deployed, documented, and portfolio-ready.

Decision Support

Clinical Decision Support System

Integrates patient vitals, lab results and EHR history to provide real-time risk-stratified recommendations.

Radiology AI

X-Ray Disease Detection

CNN-based model trained on NIH ChestX-ray14 detecting 14 thoracic pathologies with Grad-CAM overlays.

Ophthalmology

Retinal Disease Screening

YOLO-based detection of diabetic retinopathy, macular degeneration and glaucoma from fundus images.

Pathology

Parasite & Malaria Detection

Real-time microscopy analysis to identify parasite eggs and malaria-infected red blood cells.

GenAI + RAG

Healthcare RAG Assistant

LangChain-powered assistant grounded in clinical guidelines (NICE, WHO) with hallucination guardrails.

NLP

Medical Research Assistant

PubMed paper mining, automatic evidence summarisation and PICO framework extraction.

Conversational AI

Patient Support Chatbot

BioBERT-powered chatbot handling symptom checking, medication queries and appointment booking.

Analytics

Hospital Analytics Dashboard

Interactive Plotly dashboard visualising KPIs: bed occupancy, LOS, readmission rates, revenue cycle.

Artificial Intelligence
for Healthcare Professionals

Healthcare AI Domains

Detailed 12-Week Learning Path

Build Real Clinical AI Systems

AI Developer, Researcher & Entrepreneur

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