Status, challenges, and future directions of machine learning in the management of epilepsy: a systematic review and meta-analysis.

BACKGROUND: Despite advances in epilepsy treatment options, selecting the appropriate therapy for an individual with epilepsy is a process of trial and error. Machine learning holds the potential to support clinical decision making. We aimed to provide an overview of the role of machine learning in epilepsy management and discuss future directions. METHODS: In this systematic review and meta-analysis, we searched Embase, MEDLINE, Scopus, and Web of Science from database inception to March 31, 2025, for human-only randomised controlled trials, cohort studies, and case-control studies predicting antiseizure medication outcomes, drug-resistant epilepsy, epilepsy surgery outcomes, and epilepsy surgery candidacy in populations with clinician-confirmed diagnosis of epilepsy. Studies on diagnosis, epilepsy classification, seizure prediction, engineering, and technical aspects of electroencephalograms, neuroimaging, or seizure detection by electrocardiogram or wearable devices were excluded to ensure clinical relevance. Summary data were extracted from published reports. Reporting quality and risk of bias were assessed with TRIPOD+AI and PROBAST, respectively. Meta-analysis was conducted by pooling the area under the receiver-operator characteristic curves (AUCs) of the best model of each study when CIs were available to assess performance. This study was registered with PROSPERO (CRD42023442156). FINDINGS: A total of 16 771 studies were identified, and 135 were included in the systematic review (33 [24%] that predicted antiseizure medication outcomes, 12 [9%] that predicted the development of drug resistance, 79 [59%] that predicted epilepsy surgery outcomes, nine [7%] that predicted epilepsy surgery candidacy, and two [1%] that predicted both antiseizure medication and epilepsy surgery outcomes). Only ten (7%) studies satisfied 70% or more of the subitems in the TRIPOD+AI reporting guidelines checklist, reflecting an overall inadequacy of most of the studies. All the included studies were rated high for overall risk of bias. The pooled AUC of the best-performing models in each study with available data was 0·82 (95% CI 0·77-0·88) in predicting antiseizure medication outcomes, 0·82 (0·76-0·88) in predicting epilepsy surgery outcomes, and 0·94 (0·92-0·96) in predicting epilepsy surgery candidacy. Studies had very high or high heterogeneity (studies predicting antiseizure medication outcomes I2=99·48%, p<0·0001; studies predicting epilepsy surgery outcomes I2=98·94%, p<0·0001; studies predicting epilepsy surgery candidacy I2=85·98%, p<0·0001). The AUCs of the models predicting drug-resistant epilepsy ranged from 0·76 to 0·99 for internal validation. INTERPRETATION: Although machine learning shows promise in predicting epilepsy treatment outcomes, the high heterogeneity and bias-particularly in small sample sizes, handling of missing data, and scarcity of studies with external validation-limit its clinical applicability. Future research should focus on larger, diverse datasets and standardised minimum reporting. Prospective trials are needed to evaluate machine learning models in real-world settings. FUNDING: Australian Government National Health and Medical Research Council.