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The Laboratory of Artificial Intelligence in Health
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AI for Health

University 4 credits equivalent

Overview

Artificial intelligence (AI) affects and changes various aspects of society and its activities. Healthcare is also at the beginning of this transformation. The potential use of AI technologies in healthcare needs healthcare professionals with knowledge of AI to enable interactive and explanatory AI and ensure the quality of AI-based systems to increase patient safety. Knowledge of AI is also important for people involved in decision-making, procurement and implementation of AI-based systems. The course introduces and provides basic knowledge about artificial intelligence (AI) and its application in health care.

Objectives

Upon successful completion of the course, participants will be able to:

  • Explain the evolution of AI in public health and healthcare: Discuss the history and impact of AI, particularly in the context of public health statistics and methodologies.

  • Identify and apply AI methodologies: Understand fundamental AI techniques, including supervised learning, unsupervised learning, and neural networks, and evaluate their applications in public health and healthcare scenarios.

  • Evaluate AI model performance: Critically assess the appropriateness and accuracy of AI models in health-related settings through validation techniques.

  • Address ethical and bias considerations: Discuss the health implications of AI, including issues of bias, fairness, and causality within AI modeling.

  • Analyze real-world applications: Provide examples of AI use cases in public health and healthcare, such as disease surveillance, predictive analytics, personalized medicine, and healthcare logistics optimization.

Content 

In this course, students will learn about the basics of AI and its application in healthcare such as medical image analysis, data analysis and data extraction, natural language processing and decision support systems. The course will also address ethical issues and data protection issues, regulations and entrepreneurship aspects of AI in healthcare. 

Teaching methods

The course uses different learning activities, such as online lectures, interactive sessions live and online, and individual project work.

AI Applications in Medical Imaging

AI applications in medical imaging involve the use of machine learning and deep learning algorithms to analyze and interpret medical images, enhancing diagnostic accuracy and efficiency. AI can assist in detecting and diagnosing various conditions, including cancers, cardiovascular diseases, neurological disorders, and musculoskeletal abnormalities, by identifying subtle patterns in images that may be missed by the human eye. Convolutional neural networks (CNNs), a type of deep learning model, are particularly effective in tasks such as image classification, segmentation, and detection. These AI systems can automatically analyze X-rays, MRIs, CT scans, and other imaging modalities, providing radiologists with tools for faster and more accurate diagnoses. Additionally, AI applications in medical imaging facilitate personalized treatment plans, enable earlier disease detection, and assist in monitoring disease progression, ultimately improving patient outcomes and optimizing healthcare workflows.

 AI Applications in Public Health Modeling 

 AI applications in public health modeling involve using machine learning, deep learning, and other AI techniques to enhance the prediction, prevention, and management of health-related issues. These models can analyze large datasets to identify patterns, predict disease outbreaks, optimize resource allocation, and personalize treatment plans. For example, AI can model the spread of infectious diseases, predict the impact of interventions, and simulate healthcare system responses to various scenarios. Additionally, AI aids in the analysis of epidemiological data, improving the efficiency of surveillance systems, identifying risk factors for diseases, and supporting evidence-based decision-making for public health policies.
[Picture]
Olawade et al. 2023

AI Applications in Disease Surveillance

AI applications in disease surveillance leverage advanced machine learning algorithms and data analytics to enhance the monitoring, prediction, and management of diseases. These AI-driven systems can process vast amounts of data from various sources, such as health records, social media, and real-time sensors, to detect emerging health threats early. AI models are used to identify patterns in disease spread, predict outbreaks, and track epidemics, providing valuable insights for timely interventions. For example, AI can analyze geographic and demographic data to predict where diseases are most likely to spread, or it can use natural language processing to monitor and analyze news and social media for early signs of outbreaks. Furthermore, AI can assist in real-time surveillance, enabling public health agencies to respond quickly and allocate resources more effectively
[Picture]
Anjaria et al. 2024

FAQs

General Questions

  1. What is the main focus of the course?
    The course focuses on introducing participants to artificial intelligence (AI) and its applications in public health and healthcare. It covers foundational AI concepts and shows how these techniques can be used to solve real-world health problems.

  2. Do I need prior knowledge of AI or programming to take this course?
    No, prior knowledge of AI or programming is not required. The course is designed for beginners who may not have experience with AI or coding.

  3. What are the prerequisites?
    You need a basic understanding of statistics and health research methods, but no advanced skills in AI or programming are necessary.

  4. How is this course different from other AI courses?
    This course is unique because it specifically focuses on the applications of AI in public health and healthcare. It combines theoretical knowledge with real-world public health examples and hands-on practice, which is not common in most general AI courses.

  5. What will I gain from this course?
    By the end of the course, you’ll understand how AI is used in public health, be familiar with key AI methods, and be able to assess and apply AI models to real-world health scenarios. You’ll also learn how to handle ethical concerns related to AI in healthcare, such as bias and fairness.

Course Content and Structure

  1. How long is the course?
    The course is equivalent to 4 credit hours and is delivered over the 2025-26 session. The virtual format offers monthly sessions, so you can attend as your schedule allows.

  2. How are the classes structured?
    Each class includes lectures on AI concepts, followed by practical activities where participants work with AI tools and techniques. There are also discussions on real-world case studies and readings from scientific papers.

  3. What topics are covered?

    • History of AI in public health
    • Key AI methods (supervised learning, unsupervised learning, neural networks)
    • Evaluation of AI model performance
    • Ethical issues like bias in AI
    • Real-world applications of AI in healthcare (e.g., disease surveillance, predictive analytics)

  4. Will there be hands-on practice?
    Yes, the course includes hands-on exercises where participants will work with AI tools and health data. This practice will help you apply what you learn to public health problems.

  5. Is there a final project or exam?
    While there’s no final exam, there may be assessments like quizzes or small projects to test your understanding. These assignments will help you apply AI techniques in practical scenarios.

Course Logistics

  1. How is the course delivered?
    The course is available both in-person (by invitation from universities) and virtually. The virtual sessions are held every month, so you can join remotely.

  2. How do I access the course materials?
    All materials, including lecture slides, code, and reading materials, will be posted on Flock/Slack, which serves as the main platform for communication and sharing resources.

  3. What technology do I need?
    You will need a reliable internet connection for the virtual sessions, a computer for accessing course materials, and software that can run basic AI models (guidance on this will be provided).

  4. How do I communicate with the instructor?
    You can contact Md. Jubayer Hossain, the course instructor, via email ([email protected]) or during his Zoom office hours (Fridays from 9 pm–10 pm). For quick questions, you can message him via Flock/Slack.

  5. Is attendance mandatory?
    Regular attendance is encouraged, especially for the live virtual sessions, as it helps you stay engaged with the course. If you miss a session, materials will be shared online.

  6. How do I ask questions during the course?
    You can ask questions during live sessions, in Flock/Slack, or directly during office hours. Flock/Slack will be the primary platform for communication with the instructor and other participants.

Enrollment and Costs

  1. How do I enroll?
    Enrollment details will be provided by CHIRAL or the universities offering the in-person format. For the virtual course, you can contact Md. Jubayer Hossain for registration information.

  2. Is there a course fee?
    Fee details will be shared during enrollment. As a participant, I would suggest reaching out to the instructor for more specific information on this.

  3. Can I receive a certificate after completing the course?
    Yes, participants who successfully complete the course will receive a certificate from CHIRAL, recognizing their understanding and skills in AI applications for public health.

Other Considerations

  1. Can I take the course if I am not from a health background?
    Yes, the course is designed to be accessible to participants from various fields, not just health professionals. The focus is on making AI concepts easy to understand and applicable to public health.

  2. How can I best prepare for the course?
    Reviewing basic statistics and health research methods will help, but no intensive preparation is necessary. All the essential concepts will be introduced during the course.

  3. Will there be networking opportunities with other participants?
    Yes, Flock/Slack will be a platform for group discussions, sharing insights, and networking with fellow participants.

  4. What if I fall behind?
    All sessions are recorded, and materials are posted online, so you can catch up if you miss a class or need to review content.

  5. Are there group projects?
    While it depends on the specific iteration of the course, there may be opportunities for group collaboration, especially during hands-on activities or discussions about case studies.

  6. How does this course help in my career?
    AI is rapidly growing in importance, especially in healthcare. This course will equip you with the knowledge and skills to leverage AI in public health research and practice, giving you an edge in careers related to healthcare, research, and health innovation.

By taking this course, you'll gain practical, relevant skills in AI for public health that are applicable in both academic and professional settings.