Offered: Fall 2025 (current)
Introduction to AI in Healthcare: AI applications in medicine, benefits and challenges, role of AI in clinical decision-making; Medical Data Processing: electronic health records (EHR), medical imaging, genomics, wearable sensor data; Machine Learning for Diagnosis and Prognosis: disease prediction, anomaly detection, personalized treatment plans; Deep Learning in Healthcare: CNNs for medical imaging, NLP for clinical notes, transformers in drug discovery; AI for Medical Imaging: image segmentation, classification, radiology, pathology, dermatology applications; Natural Language Processing in Healthcare: clinical text processing, medical chatbots, summarization of health records; AI in Drug Discovery and Genomics: protein structure prediction, biomarker discovery, drug repurposing, precision medicine; Healthcare Robotics: surgical robots, rehabilitation robotics, AI-powered prosthetics; AI in Remote Patient Monitoring: wearable technology, predictive analytics for chronic diseases, telemedicine; Reinforcement Learning in Healthcare: treatment optimization, dynamic patient monitoring, robotic surgery control; AI for Healthcare Operations: hospital resource management, predictive scheduling, workflow optimization; Explainability and Trust in AI: model interpretability, bias detection, ethical considerations, patient trust in AI decisions; AI Ethics and Regulations: data privacy, bias in medical AI, regulatory compliance (HIPAA, GDPR, FDA approvals); Case Studies and Real-World Applications: AI-powered diagnostics, pandemic response, AI-assisted surgeries; Challenges and Future Trends: integration with healthcare systems, generalizability, advancements in AI-driven medical research.
The core objectives of this course are to:
To provide a comprehensive understanding of core AI concepts and their application to medical data and clinical decision-making
To develop proficiency in processing and analyzing diverse medical data types, including EHR, imaging, and genomic data
To equip students with the skills to design, implement, and evaluate machine learning and deep learning models for diagnosis, prognosis, and drug discovery
To gain expertise in applying AI techniques, particularly CNNs and NLP, to medical imaging and clinical text processing
To understand how AI is transforming drug discovery, biomarker identification, and precision medicine
To critically analyze the ethical and regulatory implications of AI in healthcare, including data privacy and algorithmic bias
To gain knowledge on the applications of AI in robotics and remote patient monitoring.
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