Overview

Artificial intelligence is redefining healthcare by improving diagnosis, accelerating research, and supporting clinical decision-making. This course explores how advanced AI technologies such as natural language processing (NLP), generative AI, and computer vision transform medical practice, data analysis, and patient care. You’ll learn how NLP extracts insights from clinical notes, how generative models produce structured medical content and decision support recommendations, and how computer vision powers diagnostic imaging and multimodal AI applications. Throughout the course, you’ll engage in guided, hands-on labs that bridge theory with real-world application. You will use Jupyter Notebook files in a Google Colab environment to complete the labs. Your learning journey culminates in a final project where you’ll build an end-to-end system that demonstrates practical and ethical use of AI in healthcare. By the end, you’ll be ready to design impactful AI solutions that enhance care delivery and innovation in healthcare.

Syllabus

Natural Language Processing for Clinical Data

In this module, you will explore advanced natural language processing (NLP) techniques used to extract meaningful insights from clinical text. The module begins by examining how NLP transforms unstructured medical notes into structured data that supports clinical decision-making. You will learn how transformer-based models such as BERT, BioBERT, and ClinicalBERT enable key tasks like entity recognition and information extraction. Through guided labs, you will build end-to-end NLP pipelines that preprocess and structure clinical information for real-world applications. You will also explore how NLP powers automated medical coding, clinical documentation, and decision support systems in healthcare workflows. The module concludes with a look at key implementation challenges, including data privacy, model integration, and workflow alignment, preparing you to design NLP solutions that enhance accuracy and efficiency in healthcare.

Generative AI for Medical Content and Decision Support

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.

Computer Vision and Multimodal AI in Medical Imaging

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.

Final Project, Final Exam, and Wrap-up

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.