AI in Medical & Healthcare

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

17 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Learn how AI transforms healthcare with hands-on projects in disease detection, medical imaging, and health prediction. Gain practical skills in building and deploying AI models to enhance patient care and healthcare systems. AI is revolutionizing healthcare, offering innovative solutions for disease detection, patient management, and medical imaging. In this course, you will learn how to apply AI to real-world healthcare challenges. The journey begins with an introduction to foundational AI concepts, followed by hands-on tutorials for building AI models for personal health assistants and mental health support chatbots. You’ll dive into advanced applications like pneumonia detection from chest X-rays, skin cancer identification, and heart disease prediction. As you progress, you'll learn how to train models to analyze medical imagery, such as MRI scans for brain tumor classification and retina images for diabetic retinopathy. The course also covers model training for intensive care unit mortality predictions and more. Each module is designed to give you practical experience in working with medical data, from preparation and training to deployment. By the end of this course, you’ll have the skills to develop AI solutions that improve healthcare delivery and outcomes. You’ll be equipped to implement AI-driven applications that assist in diagnostics, patient care, and medical research, empowering you to contribute meaningfully to the healthcare industry’s AI evolution. This course is designed for healthcare professionals, data scientists, AI enthusiasts, and anyone looking to explore the intersection of artificial intelligence and healthcare. It’s perfect for those interested in developing skills to apply AI in real-world medical applications. A basic understanding of Python and machine learning is recommended, though the course also covers foundational concepts to ensure learners are equipped for success. This course covers AI applications in healthcare, from basic concepts to hands-on projects in disease detection and medical image analysis. You'll learn to train and deploy AI models, gaining practical skills to improve healthcare solutions and patient outcomes through real-world applications. This course is based on AI in Medical & Healthcare, by Augmented AI, Ritesh Kanjee. This video is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Welcome to the Course
- This module introduces learners to the course structure, key objectives, and available support resources. It also outlines the benefits of course completion and the certification process, helping learners understand what to expect and how to succeed.
Module 1 - Application 1 - Personal Health Assistant
- This module provides an overview of building a Personal Health Assistant, covering data preparation, model fine-tuning, and deployment. Learners will understand the steps involved in creating a health-focused AI application and gain practical skills in data cleaning, model training, and user interface integration.
Module 1 - Application 2 - Mental Health Therapist Chatbot
- This module provides an overview of building a mental health support chatbot using open source large language models. Learners will explore key steps such as setting up the environment, managing conversations, and integrating APIs for knowledge retrieval. The focus is on developing functional and ethical chatbot solutions in a web application framework.
Module 2 - Application 3 - Missing Medical Tools Detection
- This module introduces learners to the development of a medical tool detection system using computer vision techniques. It covers the training of models, integration of YOLO V9 with language models, and the implementation of a user-friendly interface using Gradio and LangChain. Learners will gain practical skills in building and deploying AI solutions for healthcare applications.
Module 2 - Application 4 - Pneumonia Detection from Chest X-Rays
- This module explores the process of detecting pneumonia using chest X-rays, covering model training, system implementation, and key concepts in medical image interpretation with AI tools.
Module 2 - Application 5 - Skin Cancer Detection
- This module explores the development and deployment of an AI-powered system for detecting skin cancer. Learners will gain insights into the technical components and workflow involved in building such a system, including model selection, user interface design, and deployment strategies.
Module 2 - Application 6 - IR Camera Fever Detection
- This module explores the use of infrared camera technology for fever detection, covering system design, image processing, and data storage. Learners will gain hands-on understanding of how to build and implement a fever detection system using Python and common computer vision techniques.
Module 3 - Application 7 - Brain Tumor Classification Using MRI Imagery
- This module explores the process of classifying brain tumors using MRI imagery, covering model training, implementation, and integration with AI applications. Learners will gain hands-on understanding of deep learning workflows and medical image analysis techniques.
Module 3 - Application 8 - Diabetic Retinopathy Classification Using Retina Images
- This module explores the application of AI in detecting diabetic retinopathy using retina images. Learners will gain knowledge on model development, data preprocessing, and responsible AI deployment in healthcare settings. The content bridges theory and practical implementation in medical image classification.
Module 4 - Application 9 - Heart Disease Prediction
- This module explores how machine learning is applied in heart disease prediction, focusing on key concepts such as model selection, data preprocessing, and user experience design in healthcare. Learners will gain an understanding of the role of analytics in preventive medicine and how to integrate these tools effectively.
Module 4 - Application 10 - Diabetes Prediction
- This module provides an in-depth look at the development and application of a Diabetes Prediction model. Learners will explore data preparation, model deployment, and performance evaluation techniques. The content emphasizes practical AI implementation in healthcare settings.
Module 4 - Application 11 - Mortality Prediction for Intensive Care Units
- This module explores the development and implementation of a mortality prediction system for intensive care units, focusing on data handling, modeling techniques, and system deployment using machine learning. Learners will gain practical insights into building healthcare-based predictive models and understanding their real-world applications.
Module 5 - Application 12 - Brain Tumor Segmentation Using 3D-MRI Imagery
- This module provides hands-on training in segmenting brain tumors using 3D-MRI imagery, covering the setup, model pipeline building, and visualization techniques. Learners will gain practical skills in medical image processing and front-end display integration.
Module 5 - Application 13 - PolyP Segmentation on Multi-Class Images for Gastrointestinal
- This module focuses on the practical implementation of PolyP segmentation for gastrointestinal imaging, covering model setup, pipeline building, output analysis, and frontend integration. Learners will gain hands-on experience with deep learning workflows and data visualization techniques.
Bonus 1 - Cell Nuclei Segmentation Application
- This module explores the application of deep learning in cell nuclei segmentation, covering best practices, model architecture, data handling, and performance evaluation techniques in biological image analysis.
Module 6 - Application 14 - Breathing Rate Monitor
- This module explores the development and implementation of a Breathing Rate Monitor application, focusing on key components like face detection, signal processing, and system deployment using Python and Streamlit. Learners will gain practical knowledge on how to design and assess such systems for respiratory health monitoring.
Bonus 2 - Heart Rate Measurement Using YOLOR-v7
- This module explores the use of the YOLOR-v7 model for heart rate measurement, focusing on practical applications in healthcare. Learners will gain hands-on knowledge of how to implement the model and understand its relevance in real-world scenarios.