This course was presented in person at World Sleep 2025 in Singapore.

To access the session recording, navigate to the content tab and click the view video button. 

Session Schedule

Find a specific presentation in the course by navigating to the timestamp indicated below.

0:00:00
Introduction
Amir Sharafkhaneh (United States)

0:14:45
Mastering machine learning (ML): The future of sleep data analysis
Javad Razjouyan (United States)

0:56:30
Generative AI and LLM:
Shirin Shafazand (United States)

1:43:45
Deep learning decoded: Elevating sleep analysis to the next level
Henri Korkalainen (Finland)

2:25:15
Big data, bigger impact: Transforming sleep research with AI
Ju Lynn Ong (Singapore)

3:12:00
AI in action: Revolutionizing the diagnosis of sleep disorders
Sulaiman S. Alsaif (Saudi Arabia)

3:47:00
Personalized sleep medicine: AI-powered treatment strategies
Amir Sharafkhaneh (United States)

4:36:15
Next-gen sleep monitoring: AI and wearable technology
Thomas Penzel (Germany)

5:21:15
AI's role in scientific writing and peer-review: Balancing benefits and risks
Ahmed BaHammam (Saudi Arabia)

6:09:00
Limitations, legal aspects and dangers of AI use
Haitham Jahrami (Bahrain)

6:25:45
Question and answer

Course Summary:

Artificial intelligence (AI) represents a valuable new tool for sleep research and sleep medicine.  AI can enhance sleep disorders detection, differential diagnosis, treatment, and prognosis. In this manner, AI potentially promises individualized care.

This course will review AI’s techniques, limitations, implementation, and its socio-legal complexities.  Course participants will acquire a better understanding of AI and its applications.  AI can assist forward-thinking clinicians, researchers, and healthcare professionals in their respective fields.

Experts will explain “how” and “where: of AI applications in sleep medicine. The course is divided into two modules: the first will attempt to demystify AI technology, providing attendees with the technical basics needed to understand and how to apply AI tools. The second will illustrate several clinical applications related to sleep disorders’ diagnostics, therapeutics, and patient care.

Whether you're a clinician looking to enhance your practice with AI-driven tools, a researcher aiming to innovate in the field and/or simply curious about the future of sleep medicine, this course will serve your interests. It will hopefully also empower you to participate in what way become technology’s next big thing.

Learning objectives:

Upon completion of this activity, participants will be able to:
• Explain machine learning, its applications, and its pitfalls as it relates to sleep medicine
• Explore what LLM and generative AI are, investigate their applications, and evaluate their shortcomings as they relate to sleep medicine
• Examine the concept of deep learning, its applications in sleep analysis, and its pitfalls related to sleep medicine
• Analyze big data, its uses, and potential shortcomings in the field of sleep medicine
• Identify examples of AI use in advancing the diagnosis of sleep disorders and evaluate the future of AI in the diagnosis of sleep disorders
• Describe examples of AI use in the treatment of sleep disorders and assess the future of AI in initiation and follow-up of sleep disorders’ therapy
• Review wearable technology, its applications, and shortcomings as they relate to sleep medicine
Evaluate the value of AI in scientific writing and peer review, including its strengths, weaknesses, and legal implications
Discuss legal aspects and shortfalls of the use of AI in healthcare, with a specific focus on sleep medicine