“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”
The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.
For sleep medicine, the initial applications of AI in this field have been innovative and promising. 2023;27:39). Pépin and colleagues (JAMA Netw Open. 2020;3:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.
AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.
New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.
AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.
Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.