Artificial Intelligence-Based Detection of Obstructive Sleep Apnea Using Acoustic Signals: Insights from Recent Studies and Future Directions

Background and Objectives: Acoustic signals offer accessible inputs for artificial intelligence (AI) methods for obstructive sleep apnea (OSA) assessment. This narrative review examines how acquisition, modeling and output construction affect screening, event detection and severity estimation. Materials and Methods: Studies published or available online from 2022 to 29 September 2026 were identified through iterative retrieval, citation checking and supplementary PubMed and Europe PMC searches. Clinical evidence and within-study design comparisons were synthesized qualitatively, with structured appraisal of study quality and applicability. Results: Published meta-analyses reported approximately 90% sensitivity and 87% specificity for overnight audio and 83% for both measures for awake speech (rounded pooled estimates). Evaluation design and disease prevalence were associated with overnight-audio sensitivity; all speech studies used professional microphones in hospitals. A prospective home study testing a previously developed model in a separate cohort obtained complete recordings in 101/117 attempts and reported severe-OSA sensitivity/specificity of 93.3/94.4% on iOS. A multicenter snoring study obtained usable recordings in 121/152 recruits; a severe-OSA area under the receiver operating characteristic curve of 0.964 coexisted with apnea–hypopnea index agreement limits of −41.25 to +23 events/h. Component comparisons linked performance to features, fusion and patient-output construction, although most were internally validated. Conclusions: Acoustic AI may support screening and referral, but routine clinical value depends on recording completion, performance in the intended population and added benefit over accessible clinical tests. Narrative selection and heterogeneous validation preclude a ranking of clinical effectiveness.