The Use of Artificial Intelligence in Neonatal Seizure Detection: An Artificial Intelligence-Assisted Systematic Review.

BACKGROUND: Artificial intelligence (AI) is increasingly used in health care. We systematically reviewed evidence on the accuracy of AI in detecting neonatal seizures. METHODS: We searched PubMed and CENTRAL (covering Medline, EMBASE, CINAHL and trial registries) (29 December 2025) for studies that evaluated the use of all AI applications for detecting and managing neonatal seizures, screened articles using ASReview (a MLA-based platform) and assessed risk-of-bias using Cochrane and QUADAS 2 tools. As data precluded meta-analysis, we performed narrative synthesis. RESULTS: We included 26 studies out of 2051 articles. One RCT (n = 258) evaluated an automated algorithm (ANSeR) and showed no differences in seizure burden (minutes) (mean difference (MD): -14.5, 95% CI -37.8, 15.9), number with seizures (risk ratio (RR) 0.86, 95% CI 0.57, 1.28) and diagnostic accuracy (%) (sensitivity: -8.2, 95% CI -25, 7.7; specificity: -4.8, 95% CI -14.1, 4.6), although higher seizure-hour (%) were detected with ANSeR (difference: 20.8, 95% CI 3.6, 37.1) (low-certainty evidence). Twenty-five validation studies (n = 1021) reported MLAs with varying sensitivity (32%-98.6%) and specificity (70.1%-100%) in seizure detection. DISCUSSION: Evidence from one RCT shows no clear improvements in seizure-related outcomes, while preliminary validation studies show promising but varied MLA performances. However, most studies had small samples and significant methodological concerns including unrepresentative population and unclear reference standards, and lack of clinical validation. The current evidence is therefore insufficient to inform clinical practice. Future studies should validate the findings by enrolling representative high-risk neonates and assess long-term patient-important outcomes. TRIAL REGISTRATION: PROSPERO: CRD42021243455.