We have earlier explored how AI is boosting healthcare systems worldwide, especially in this age of the pandemic. The applications of AI in medicine know no bounds. Reports suggest when AI is coupled with other manner of health data, it can provide some valuable insights which can help healthcare providers treating sleep disorders, and it will also help in delving deeper into the human understanding of the role of sleep in maintaining good health. Not only this, it is widely accepted that AI can also streamline everyday working of the medical care system and put patient care at the forefront of the priorities of the sleep disorders team.
What is required in the current state of affairs is to develop best practices which will help in incorporating the new and evolving technology into the day-to-day operations of the healthcare providers and other stakeholders at the same time maintaining transparency and quality standards. When mobilized in a systematic way with human inputs, AI has the potential to improve the working of sleep medicine and use sleep science towards the upkeep of health and well-being of the patients.
It is no secret that the review of sleep disorders and the course of their treatment lies in the use of polysomnography or sleep study which is a test to diagnose sleep disorders. Polysomnography brings home a huge amount of labeled electrophysiological data. Sleep science can thus benefits by the use of big data by the way of creation of AI-based programs which can help in the following goals: a) A precise classification and diagnosis of disorders as well as diseases b) disease prediction and prognosis, c) division of the disease into subtypes, d) accurate and automated instrumentation via sleep scoring, and e) customization of treatment and much more.
By their very nature, ML algorithms and programs pick up patterns by adjusting variables and improve performance of prediction, classification, clustering etc. All this helps in a greater understanding of the Therefore, they provide powerful tools for understanding links within datasets available.
Electrophysiological data, which is derived from PSG recordings in huge amounts, acts as the base for AI applications. When this data is combined with factors such as genetic information, demographics, as well as behavioral and lifestyle data, AI becomes a powerful tool in providing hitherto unknown insights for an informed diagnosis and clinical care for patients with sleep disorders.
Another area in which sleep sciences stand to benefit from AI is the health of the population. AI can help with deeper insights and understanding about the intrinsic role of sleep on human health on a very big scale.