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Background <p>AI-enhanced neuroimaging is rapidly developing, with applications in tumor segmentation, hemorrhage detection, vascular lesion mapping, tractography, and augmented reality. Despite high algorithmic performance in experimental studies, few of these tools are integrated or validated into routine neurosurgeon practice. This review addresses the translational gap by assessing evidence for clinical relevance, explainability, and workflow integration, and by proposing a pragmatic framework implementation for decision support.</p> Methods <p>A narrative review was conducted of literature published between 2015 and 2025 using PubMed, Google Scholar, and regulatory reports. Included studies comprised primary studies, technical reports, and reviews on AI-driven neuroimaging tools with specific pertinence to neurosurgical diagnosis, planning, or intraoperative decision making. Extracted data was synthesized thematically across domains of technical validation and clinical benefit, explainability, and implementation feasibility.</p> Results <p>Existing AI tools demonstrate promising technical performance: tumor segmentation models achieve Dice scores &gt; 0.80, hemorrhage detection networks report sensitivities over 90% and AUCs ~ 0.95, tractography augmentation and intraoperative image registration enhance mapping accuracy, and AR overlays are increasingly feasible. Nevertheless, the majority of investigations remain retrospective, single-center studies without external validation. Explainability adoption remains variable across studies. Latency, interface design, regulatory uncertainty, and interoperability complicate the workflow integration. And few devices have undergone prospective multicenter trials or secured full regulatory approval.</p> Conclusions <p>While AI neuroimaging has clear potential to facilitate the accuracy and efficiency of current neurosurgical approaches, translation into the clinical setting necessitates robust multicenter validation, transparent explainability features, and integration into existing surgical workflows. A structured framework connecting validation, explainability, and implementation can assist neurosurgeons and institutions in determining which tools are truly ready for safe adoption from image to incision.</p>

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From image to incision: clinical validation, explainability, and workflow integration of AI neuroimaging tools for real-time neurosurgical decision support

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Godswill Uzoechina,
  • Treasure Osajiuba

摘要

Background

AI-enhanced neuroimaging is rapidly developing, with applications in tumor segmentation, hemorrhage detection, vascular lesion mapping, tractography, and augmented reality. Despite high algorithmic performance in experimental studies, few of these tools are integrated or validated into routine neurosurgeon practice. This review addresses the translational gap by assessing evidence for clinical relevance, explainability, and workflow integration, and by proposing a pragmatic framework implementation for decision support.

Methods

A narrative review was conducted of literature published between 2015 and 2025 using PubMed, Google Scholar, and regulatory reports. Included studies comprised primary studies, technical reports, and reviews on AI-driven neuroimaging tools with specific pertinence to neurosurgical diagnosis, planning, or intraoperative decision making. Extracted data was synthesized thematically across domains of technical validation and clinical benefit, explainability, and implementation feasibility.

Results

Existing AI tools demonstrate promising technical performance: tumor segmentation models achieve Dice scores > 0.80, hemorrhage detection networks report sensitivities over 90% and AUCs ~ 0.95, tractography augmentation and intraoperative image registration enhance mapping accuracy, and AR overlays are increasingly feasible. Nevertheless, the majority of investigations remain retrospective, single-center studies without external validation. Explainability adoption remains variable across studies. Latency, interface design, regulatory uncertainty, and interoperability complicate the workflow integration. And few devices have undergone prospective multicenter trials or secured full regulatory approval.

Conclusions

While AI neuroimaging has clear potential to facilitate the accuracy and efficiency of current neurosurgical approaches, translation into the clinical setting necessitates robust multicenter validation, transparent explainability features, and integration into existing surgical workflows. A structured framework connecting validation, explainability, and implementation can assist neurosurgeons and institutions in determining which tools are truly ready for safe adoption from image to incision.