In this module, you will explore the use of generative AI in healthcare to enhance clinical reporting, decision support, and patient engagement. The module begins by introducing large language models (LLMs) and advanced prompting techniques, demonstrating how these models can be adapted and fine-tuned for medical applications. You will learn how generative AI produces structured radiology and pathology reports, supports clinical decision-making, and generates personalized treatment recommendations. Through hands-on labs, you will build systems that automate medical report generation (Lab 5) and develop conversational AI chatbots for patient education and triage (Lab 6). The module also covers best practices for evaluating the accuracy, clinical utility, and ethical considerations of AI-generated content, equipping you to implement generative AI solutions that improve efficiency, safety, and patient-centered care in healthcare settings.

In this module, you’ll explore how computer vision and multimodal AI are revolutionizing medical imaging and diagnostics. You’ll learn how deep learning models such as CNNs and Vision Transformers detect diseases, identify anatomical structures, and enable applications like surgical guidance and patient monitoring. You’ll also examine how multimodal AI combines imaging data with clinical notes and lab results to enhance diagnostic accuracy. Through case-based examples, you’ll analyze model architectures, workflows, and evaluation methods, as well as key deployment factors like performance, regulatory standards, and workflow integration. By the end of this module, you’ll be able to evaluate and design AI-driven imaging workflows that improve clinical accuracy and patient outcomes.

This final module integrates advanced AI technologies learned throughout the course to address a comprehensive healthcare challenge. You will develop a multimodal AI solution combining natural language processing for clinical text analysis, generative AI for medical content creation, and computer vision for diagnostic imaging. The project emphasizes real-world clinical application, requiring learners to build an end-to-end pipeline that processes diverse healthcare data types, generates actionable insights, and presents findings in a clinically relevant format suitable for healthcare professionals and stakeholders.