AI in Healthcare

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🎓 Course Modules:

🧠 Module 1: Introduction to AI in Healthcare

Overview of healthcare challenges and opportunities

AI’s role in medical systems and hospital workflows

Regulatory frameworks (HIPAA, GDPR, FDA)

🏥 Module 2: Electronic Health Records (EHR) & Data Handling

EHR structure and clinical data types

Data cleaning and preprocessing techniques

Patient privacy and anonymization

🧪 Module 3: Predictive Analytics in Medicine

Disease prediction using machine learning

Risk scoring and early diagnosis

Case study: Diabetes and heart disease forecasting

🧬 Module 4: Medical Imaging & Computer Vision

Image classification and segmentation in radiology

Detecting tumors, fractures, and anomalies

Tools: OpenCV, PyTorch, TensorFlow in medical imaging

🔍 Module 5: Natural Language Processing in Healthcare

Analyzing clinical notes and prescriptions

Named Entity Recognition for symptoms, drugs, and conditions

NLP tools: spaCy, BERT for healthcare

🤖 Module 6: AI-Powered Robotics & Assistive Tech

Surgical robots and AI-assisted procedures

Wearable AI for patient monitoring

Rehabilitation and elderly care innovations

🧰 Module 7: Tools and Platforms for Healthcare AI

Google Health, IBM Watson, and open datasets

Building models with Scikit-learn and TensorFlow

Integrating AI into hospital systems

⚖️ Module 8: Ethics, Bias, and Fairness in Medical AI

Avoiding bias in clinical algorithms

Informed consent and explainable AI

Equity in AI access and treatment delivery

🧪 Module 9: AI in Drug Discovery & Genomics

Machine learning for molecule prediction

AI in vaccine and treatment development

Genomic data analysis and precision medicine

✅ Module 10: Final Capstone Project

Choose from projects like:

AI diagnosis system for X-rays

Predictive model for patient readmission

NLP-based medical chatbot

Final review and healthcare impact report

 

🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

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