AI Tools for Healthcare Professionals

To equip healthcare professionals, clinicians, and medical researchers with advanced practical skills in Generative AI, Clinical Machine Learning, Healthcare Predictive Analytics, and Explainable AI, enabling them to develop and critically evaluate AI-assisted tools for clinical research, patient risk assessment, and evidence-based decision support.

• Understand advanced applications of AI, Generative AI, and machine learning in modern healthcare and clinical research.
• Analyze electronic health records (EHR), laboratory data, and clinical indicators using Python-based healthcare analytics.
• Develop AI-powered disease prediction and patient risk stratification models using Random Forest and XGBoost.
• Apply ChatGPT, Medical LLMs, and prompt engineering for evidence-based medical research and clinical information retrieval.
• Explore Retrieval-Augmented Generation (RAG) to develop source-grounded medical AI research assistants.
• Apply Explainable AI (SHAP) to interpret clinical predictions and identify influential patient risk factors.
• Develop interactive AI-assisted clinical decision-support dashboards while considering model reliability, patient privacy, and responsible AI.

📅 Day 0: Workshop Orientation — Introduction to Clinical AI, Generative AI & Healthcare Analytics

• Overview of the 3-day advanced hands-on workshop, learning objectives, and practical outcomes.
• Introduction to Clinical AI, Generative AI, ChatGPT, Medical LLMs, and healthcare predictive analytics.
• Applications of AI in disease prediction, precision medicine, clinical decision support, and medical research.
• Introduction to Google Colab, Python, PubMed, and publicly available healthcare datasets.
• Workshop workflow, Google Colab setup, access to study materials, and preparation for hands-on sessions.

📅 Day 1: Clinical AI & Disease Prediction: Machine Learning, Patient Risk Analytics & Precision Healthcare

• AI in modern healthcare: clinical decision support, precision medicine, and intelligent patient risk assessment.
• Clinical data analytics using electronic health records (EHR), laboratory results, and patient health indicators.
• AI-powered disease prediction, predictive healthcare analytics, and patient risk stratification.
• Machine learning for healthcare: Random Forest, XGBoost, and predictive modeling.
• Clinical model evaluation using ROC-AUC, sensitivity, specificity, and confusion matrices.
• Identifying important clinical biomarkers and risk factors for personalized healthcare.

🧪 Hands-On Activity:

Analyze a publicly available cardiovascular disease dataset using Python and Google Colab. Build and compare Random Forest and XGBoost models to predict patient risk, visualize clinical indicators, and evaluate model performance.

💻 Tools: Google Colab | Python | Pandas | Scikit-learn | XGBoost | Matplotlib

📅 Day 2: Generative AI in Healthcare: Medical LLMs, AI Copilots & Smart Clinical Research

• Generative AI in medicine: ChatGPT, medical large language models (LLMs), and healthcare AI assistants.
• Advanced prompt engineering for clinical research, medical information retrieval, and evidence-based healthcare.
• AI-powered medical literature analysis using PubMed and scientific databases.
• Medical NLP for clinical text summarization and extraction of relevant medical information.
• Retrieval-Augmented Generation (RAG) for evidence-grounded medical question answering.
• Building medical AI copilots for research support and healthcare knowledge retrieval.
• Identifying hallucinations, unsupported claims, and risks in medical AI outputs.

🧪 Hands-On Activity:

Use ChatGPT-style prompting and PubMed research articles to extract clinical evidence, summarize medical literature, and create a basic RAG-powered medical research assistant that answers questions using retrieved scientific sources.

💻 Tools: ChatGPT | PubMed | Python | Hugging Face | Sentence Transformers | FAISS | Google Colab

📅 Day 3: Smart Clinical Decision Support: Explainable AI, SHAP & Healthcare AI Automation

• Explainable AI (XAI) for transparent clinical predictions and healthcare decision support.
• SHAP-based interpretation of disease risk predictions and clinical feature importance.
• AI-assisted patient risk stratification and personalized healthcare analytics.
• Clinical AI dashboards for visualizing patient indicators and model-generated risk scores.
• Introduction to healthcare AI agents and automation of research-oriented workflows.
• Model bias, patient privacy, responsible AI, and clinical validation.
• Integrating predictive analytics and explainability into an AI-assisted clinical decision-support prototype.

🧪 Hands-On Activity:

Apply SHAP to interpret the Day 1 clinical prediction model, identify influential patient risk factors, and develop an interactive healthcare AI dashboard displaying risk predictions, feature explanations, and model limitations.

💻 Tools: Python | SHAP | Scikit-learn | Gradio | Plotly | Google Colab

• Doctors & Healthcare Professionals
• Medical & Healthcare Students
• Ph.D. Scholars
• Academic Researchers
• Healthcare Data Scientists
• Bioinformatics Professionals
• Pharmaceutical Professionals
• Biotechnology Professionals
• Healthcare Industry Professionals

Upon successful completion of this advanced hands-on workshop, participants will be able to:

• Clinical AI & Healthcare Analytics: Analyze clinical datasets and identify meaningful patterns in patient health indicators using Python-based AI tools.
• AI-Powered Disease Prediction: Build and evaluate machine-learning models for disease risk prediction and patient risk stratification.
• Generative AI for Medical Research: Use ChatGPT, Medical LLMs, and advanced prompting techniques for medical literature analysis and evidence extraction.
• Medical AI Copilot Development: Create a basic RAG-enabled research assistant for source-grounded medical question answering.
• Explainable AI & Clinical Interpretation: Apply SHAP to interpret patient-level predictions and evaluate influential clinical features.
• Clinical AI Dashboard Development: Design an interactive healthcare risk analytics dashboard using Gradio and Python.
• Responsible Healthcare AI: Recognize model bias, validation requirements, privacy concerns, and limitations of AI-assisted clinical workflows.