- This course focuses on applying AI techniques in healthcare for improving diagnostics, treatment plans, and patient care. Designed specifically for healthcare professionals, this course provides hands-on training to utilize AI in medical practice.
- Level: Intermediate
- Prerequisites: Medical background with basic understanding of data and analytics.
- Assessments: Micro Assessment weekly, Full Assessment in the final week.
Week 1 - AI in Medical Diagnostics
Learning OutcomeUnderstand how AI models are used to assist in medical diagnosis through image analysis.
1.1 AI for X-ray, MRI, and CT scan analysis.1.2 AI models in detecting diseases (e.g., cancers, fractures).
1.3 Practical use of AI-powered diagnostic tools in clinical settings.
Practical Component
Use AI to analyze medical images for disease detection.
Week 2 - AI for Personalized Treatment and Predictive Healthcare
Learning OutcomeExplore AI for predicting patient outcomes and creating personalized treatment plans
2.1 AI-driven predictive models for disease progression.2.2 Building personalized treatment plans using AI.
2.3 Real-time patient monitoring through AI.
Practical Component
Develop an AI-based system to predict patient recovery times based on medical data.
Week 3 - AI in Surgery and Robotic
Learning OutcomeLearn how AI-powered robotics can enhance surgical precision and assist in operations.
3.1 AI in robotic-assisted surgeries.3.2 AI models for real-time surgery guidance.
3.3 Ethical considerations in AI-powered surgery
Practical Component
Implement an AI model to assist in simulating a surgical procedure.
Week 4 - AI for Healthcare Workflow Automation
Learning OutcomeUnderstand how AI can automate administrative tasks in healthcare settings
4.1 AI for patient scheduling and resource allocation.4.2 AI-driven workflow optimization in hospitals.
4.3 Reducing errors and improving healthcare efficiency using AI.
Practical Component
Design an AI-based system to optimize patient scheduling and hospital resource management.
Week 5 - AI for Drug Discovery and Research
Learning OutcomeLearn how AI can speed up the drug discovery process and enhance pharmaceutical research.
5.1 AI models for drug target identification.5.2 Predicting drug interactions and side effects using AI.
5.3 Generative models (GANs) for molecular drug design.
Practical Component
Use AI to predict potential drug compounds based on molecular data.
Week 6 - Capstone Project and Assessment
Learning OutcomeApply AI-driven healthcare solutions to real-world problems.
6.1 Implement an AI-powered healthcare solution for diagnostics, treatment, or operational efficiency.6.2 Peer assessment and feedback.
6.3 Final project presentation and evaluation
Practical Component
Present a healthcare-focused AI solution, showcasing its impact on patient care or hospital management.