# AI enabled decision support systems in epilepsy surgery a scoping review

## TL;DR

This review maps AI tools in epilepsy surgery, finding most focus on pre-operative stages with limited data sharing and clinical integration.

## Contribution

The study provides a comprehensive scoping review of AI in epilepsy surgery, highlighting gaps in data and clinical integration.

## Key findings

- Most AI studies focus on pre-operative stages with few intra- or post-operative applications.
- AI models predominantly use small, single-center datasets and supervised CNNs.
- External validation and workflow integration are rare, limiting generalizability and clinical readiness.

## Abstract

Artificial intelligence is increasingly explored to support decision-making in epilepsy surgery, yet evidence for implementation across the epilepsy surgery pathway remains limited. We conducted a scoping review of 145 studies published between January 2018 and May 2025 to map AI-enabled decision support systems across surgical stages and clinical tasks, characterize datasets by modality, size, geographic provenance and accessibility, and synthesize modeling practices, external validation and workflow integration. The literature is heavily concentrated in the pre-operative stage, with no included intra-operative studies and relatively few post-operative applications. Most studies rely on small, single-center and non-public datasets and use supervised CNN-based models. External validation and workflow-integrated evaluation are uncommon, and only a minority of systems report semi-integrated clinical workflows. These findings highlight key gaps in generalizability, workflow readiness and equity, and inform priorities for multi-center data resources, rigorous cross-site evaluation and clinically meaningful endpoints to enable safe, scalable adoption.