Healthcare

Artificial Intelligence is transforming the healthcare industry at an unprecedented pace, causing significant changes in diagnosis, treatment, operations, and patient engagement. This three-day workshop will explore how AI technologies—such as predictive diagnosis, advancements in medical imaging, robotic process automation (RPA), robot-assisted surgeries, personalized medicine, AI agents, innovations in drug discovery, and virtual health assistants—are revolutionizing healthcare delivery. 

Participants will gain insights into the technological advancements driving these changes, examine real-world case studies, and discover how AI improves decision-making, operational efficiency, and patient outcomes. The workshop is designed to equip healthcare professionals, managers, and decision-makers with the knowledge to embrace AI-driven solutions strategically. 

• Examine the impact of AI in healthcare sectors.
• Explore how predictive diagnosis enhances early      detection and preventive care.
• Analyze AI's revolution in medical imaging accuracy.
• Describe how Robotic Process Automation (RPA)      streamlines operations.
• Evaluate the benefits of robot-assisted surgeries in precision medicine.
• Understand personalized medicine through AI-driven analytics.
• Investigate AI agents and virtual health assistants in patient interactions.
• Assess AI's role in accelerating drug discovery.
• Identify opportunities for AI integration in healthcare. 

Day 1:

- AI Ethical Considerations and Project Key Elements

- Prior Layers to AI

- Predicting Diseases with Machine Learning

- Complex Diagnoses with Neural Networks

- Diagnosing Diseases with Computer Vision

Day 2: 

- Drug Discovery with Generative Deep Learning

- NLPs and AI Agents

- Robot-Assisted Surgery

- Predictive Diagnosis with Sequential DL

- Administrative Automation with RPAs

- Future of AI in Healthcare

- AI Ethical Considerations 

- AI-driven predictive diagnosis and early disease detection. 

- Revolution in medical imaging with faster, more accurate results. 

- Robotic Process Automation (RPA) for enhanced operational efficiency. 

- Robot-assisted surgeries for precision and quicker recovery. 

- Personalized medicine through AI analysis of patient data. 

- Virtual health assistants for patient interaction and monitoring. 

- AI's impact on drug discovery and development. 

- Case studies and strategies for AI integration in healthcare. 

 This five-day intensive training will equip participants with practical knowledge and hands-on experience in statistical analysis, data preprocessing, and machine learning techniques tailored to medical datasets.

Participants will explore supervised and unsupervised learning approaches for extracting insights, predicting outcomes, and supporting medical decision-making.

The training bridges theoretical understanding with real-world healthcare applications, leveraging clinical data to uncover patterns, assess risks, and enhance predictive accuracy in diagnosis and treatment planning. 

• Apply foundational statistical methods in healthcare.
• Perform exploratory data analysis on medical datasets.
• Clean, preprocess, and visualize clinical data.
• Build and evaluate machine learning models for classification and regression.
• Use supervised learning to predict medical outcomes.
• Employ unsupervised learning for clustering and anomaly detection.
• Communicate data-driven insights in medical contexts.
• Integrate domain knowledge to improve model accuracy.
• Utilize Python and libraries like pandas and scikit-learn.

Day 1:

- Data Exploration, visualization, and statistical KPIs

Day 2: 

- Data Analysis

Day 3:

- - Supervised Learning for predictive models

Day 4:

- Supervised Learning for predictive models

Day 5:

- Unsupervised Learning and Dimensionality Reduction

- Exploratory data analysis (EDA) of patient datasets

- Data cleaning and handling missing data

- Supervised learning: logistic regression, decision trees, SVMs

- Unsupervised learning: k-means clustering, PCA

- Feature engineering for medical models

- Evaluation metrics: accuracy, precision, recall, ROC curves

- Use cases: disease prediction, risk stratification

- Live coding and prompt engineering solutions