Artificial intelligence is revolutionizing healthcare, offering groundbreaking new approaches to everything from diagnosing sleep problems to personalizing medicine. But can AI also tackle a fundamental challenge in healthcare: making complex health information clear and understandable to patients?

A recent study at the University of Michigan put this question to the test by looking at how well ChatGPT-3.5, a recent version of OpenAI’s large language model, answered common questions about sleep apnea.

The study’s authors found that ChatGPT provided accurate answers to most questions about sleep apnea, regardless of how the questions were phrased. When prompted to give more patient-friendly language or to provide statistics, the AI chatbot showed a remarkable ability to adjust its responses.

While having a virtual sleep expert at your fingertips is an exciting prospect, the journey to a fully reliable AI assistant is still unfolding. Critics say this study focused on too few prompts and didn’t fully examine whether ChatGPT’s responses match the latest research. These experts also highlight ongoing risks of misinformation and issues with AI not showing its sources.

Patient education isn’t the only place where AI is transforming sleep medicine. AI is increasingly being used to interpret the huge amount of data produced by sleep studies, a key tool in diagnosing sleep problems. Experts say that AI can help sort through the data to identify subtle sleep problems that might otherwise be missed by doctors.

AI tools are also being used to suggest personalized treatments for sleep problems based on individual sleep data. Many of these tools use AI-driven smartphone apps and wearable tech to monitor your health and provide real-time suggestions to improve your sleep.

As AI tools like ChatGPT reshape how doctors understand, communicate, and treat sleep problems, the opportunities for improving healthcare are incalculable. AI models continue to have limitations, but advances are paving the way for more effective, personalized educational tools and treatment options. This progress points to a future where accessing quality information about sleep health may be easier and more efficient for everyone.

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References
3 Sources

  1. Campbell, D. J., Estephan, L. E., Mastrolonardo, E. V., Amin, D. R., Huntley, C. T., & Boon, M. S. (2023). Evaluating ChatGPT responses on obstructive sleep apnea for patient education. Journal of Clinical Sleep Medicine, 19(12), 1989–1995.

    https://jcsm.aasm.org/doi/10.5664/jcsm.10728
  2. Kleebayoon, A., & Wiwanitkit, V. (2023). ChatGPT, obstructive sleep apnea, and patient education. Journal of Clinical Sleep Medicine, 19(12), 2133.

    https://jcsm.aasm.org/doi/10.5664/jcsm.10768
  3. Bandyopadhyay, A., & Goldstein, C. (2023). Clinical applications of artificial intelligence in sleep medicine: A sleep clinician’s perspective. Sleep & Breathing, 27(1), 39–55.

    https://link.springer.com/article/10.1007/s11325-022-02592-4

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