AI in Healthcare and Biomedical Applications
This theme explores how artificial intelligence is transforming healthcare by improving diagnosis, treatment planning, medical imaging, patient monitoring, and predictive analytics. It equips learners with the tools to build intelligent healthcare solutions using real-world datasets and AI models.
“This theme is ideal for computer science, biomedical engineering, nursing, and healthcare professionals who want to bridge the gap between clinical practice and intelligent systems. It promotes interdisciplinary collaboration and innovation in healthcare technology aligned with global initiatives like digital health and AI for Good.”
Healthcare is one of the most impactful areas of AI application. This theme introduces learners to the use of machine learning (ML) and deep learning (DL) techniques in analyzing clinical, biomedical, and imaging data to enhance patient care and decision-making. Topics covered include: Medical Image Analysis: tumor detection, segmentation, and classification using CNNs and U-Nets. Predictive Modeling: forecasting patient outcomes, disease progression, and readmissions using supervised learning. Natural Language Processing (NLP) in Healthcare: extracting insights from clinical notes, prescriptions, and patient records. AI in Medical Devices: wearable sensors, real-time monitoring, and health alert systems. Healthcare Datasets & Tools: working with real datasets like MIMIC-III, NIH Chest X-ray, and synthetic EMR data using Python, TensorFlow, and Scikit-learn. The theme also addresses challenges in healthcare AI, such as class imbalance, explainability, privacy, data integration, and model generalizability. Learners will evaluate their models not only based on accuracy but also using metrics like sensitivity, specificity, AUC, and F1-score—essential for clinical relevance.
