ReviewBioengineering (Basel, Switzerland)2024
Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Sleep disturbance in patients undergoing shoulder surgery: a current concepts review.JSES reviews, reports, and techniques · 2026Review
- Embedding Engineers in Clinical Pulmonology: Toward Earlier Clinical-Technological Integration.Bioengineering (Basel, Switzerland) · 2026Article
- Beyond Algorithms: The Case for Standardized Reporting in AI Sleep Scoring.Nature and science of sleep · 2026Article
- AI-Driven Smart Cockpit: Monitoring of Sudden Illnesses, Health Risk Intervention, and Future Prospects.Sensors (Basel, Switzerland) · 2025Review
- Sleep Medicine-What's in a Name?Journal of sleep research · 2025Review
- Artificial intelligence in sleep medicine: assessing the diagnostic precision of ChatGPT-4.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Artificial intelligence may fill the gap of underdiagnosis in sleep medicine: the role of ChatGPT-4.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.Scientific reports · 2025Article
- Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.Journal of medical Internet research · 2025Article
- MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model.Biomimetics (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Home Sleep Monitoring Technologies: Smartphone Apps, Smartwatches, and Smart Mattresses.Sensors (Basel, Switzerland) · 2025Review
- Data Processing and Machine Learning for Assistive and Rehabilitation Technologies.Bioengineering (Basel, Switzerland) · 2025Article
- Validation of a Wireless, Self-Applied Device for Sleep Recording in the Paediatric Population.Nature and science of sleep · 2025Article
- Artificial Intelligence in Sleep Medicine: The Dawn of a New Era.Nature and science of sleep · 2024Article
- Comparing polysomnography auto scoring with the standard of care in sleep medicine.Annals of thoracic medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.