Master Study AI

Final Capstone Project: AI in Healthcare

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Course Modules:

Module 1: Project Planning and Use Case Definition

Define your healthcare AI use case: NLP, imaging, diagnostics, prediction, etc.

Outline your problem, stakeholders, and value proposition

Choose appropriate tools, frameworks, and datasets

Module 2: Data Acquisition & Preparation

Identify or collect a medical dataset (e.g., MIMIC-III, ChestX-ray, DrugBank)

Perform cleaning, de-identification, and preprocessing

Ensure data privacy compliance (HIPAA/GDPR)

Module 3: Model Development & Training

Select an algorithm appropriate for your problem (e.g., CNN, BERT, XGBoost)

Train and evaluate the model using healthcare metrics (AUC, sensitivity, F1 score)

Include interpretability methods (e.g., SHAP, Grad-CAM)

Module 4: Ethics, Bias, and Validation

Assess bias and fairness across subpopulations

Perform error and risk analysis

Prepare a model transparency and accountability statement

Module 5: Deployment and Presentation

Package your model as a demo or interactive app (e.g., Streamlit, Flask, Gradio)

Prepare a technical report, executive summary, and visual insights

Record a short walkthrough or live simulation (if possible)

Module 6: Submission & Peer Review

Submit your GitHub repo, final notebook, and model demo

Review 1–2 peer projects and provide structured feedback

Earn certification and recognition from Master Study

Tools & Technologies Used:

Python

TensorFlow, PyTorch, Scikit-learn

NLP: spaCy, Hugging Face Transformers

Imaging: OpenCV, MONAI, Grad-CAM

Deployment: Streamlit, Flask, Gradio

Target Audience:

Students completing the AI in Healthcare learning track

Professionals building a portfolio for AI roles in healthtech

Researchers prototyping real-world healthcare solutions

Innovators and startup founders developing clinical AI applications

 Global Learning Benefits:

Solve real healthcare problems using end-to-end AI techniques

Apply ethical, explainable, and compliant AI development practices

Build a ready-to-showcase project for employment or academic growth

Join a global community of AI-in-healthcare innovators

 

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