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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review.

Umid Sulaimanov¹, Nafiye Sanlier¹, Ariorad Moniri²

¹Department of Neurological Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA.

Journal of Clinical Medicine

|May 13, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) in neurosurgical imaging shows progress in multicenter studies and external validation. However, AI models need better human benchmarking and calibration for clinical readiness.

Keywords:

CLAIM TRIPOD-AI artificial intelligence calibration clinical translation data leakage deep learning external validation machine learning medical imaging

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Area of Science:

Neurosurgery
Medical Imaging
Artificial Intelligence
Machine Learning
Deep Learning

Background:

Artificial intelligence (AI) is rapidly advancing in medical imaging for diagnosis, prognostication, and surgical planning.
Concerns persist regarding the methodological rigor and clinical readiness of many AI models in neurosurgery.

Purpose of the Study:

To conduct a methodological audit of AI imaging studies relevant to neurosurgery published in 2025.
To assess the clinical readiness and reporting standards of AI applications in neurosurgical imaging.

Main Methods:

Systematic review following PRISMA guidelines, searching PubMed for AI imaging studies in neurosurgery published in 2025.
Data extraction on validation, data leakage, human comparators, calibration, and CLAIM/TRIPOD-AI adherence.
Risk of bias assessment using PROBAST+AI.

Main Results:

91 studies met inclusion criteria; China led contributions, oncology was the most common domain, and MRI was the predominant modality.
External validation (75.8%) and multicenter cohorts (66.0%) were frequently reported, with low data leakage risk (93.4%).
Limited use of human comparators (18.7%), low calibration reporting (30.8%), and no studies achieved full CLAIM/TRIPOD-AI compliance.

Conclusions:

AI neurosurgical imaging studies in 2025 show progress in multicenter design and external validation.
Significant gaps remain in human benchmarking, calibration reporting, and adherence to reporting guidelines.
Further methodological development is crucial for the clinical readiness of AI in neurosurgery.