Artificial Intelligence in Neuroradiology: Transforming Diagnosis, Prognostication, and Workflow.
- 1Qure.aiKolkataWest BengalIndia.
Abstract
Artificial intelligence (AI) is reshaping neuroimaging across the entirety of the clinical workflow, encompassing patient scheduling, scan quality assurance, image acquisition and reconstruction, automated lesion detection, pathological characterization, treatment planning, and outcome prognostication. This review synthesizes contemporary evidence through a workflow-centric framework, with particular emphasis on AI tools that have achieved real-world clinical implementation. Although reported technical performance across these domains is frequently compelling, critical deficiencies persist in external validation, human-AI benchmarking, model calibration, and standardized performance reporting, collectively impeding full clinical integration. Emerging evidence suggests that the role of the neuroradiologist is transitioning toward a collaborative human-AI paradigm, rather than one of displacement.
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BibTeX
@article{ghosh2026artificial,
title = {{Artificial Intelligence in Neuroradiology: Transforming Diagnosis, Prognostication, and Workflow.}},
author = {Tamaghna Ghosh},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = oct,
doi = {10.1055/s-0046-1829088},
url = {https://europepmc.org/article/MED/42921641},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Neuroradiology: Transforming Diagnosis, Prognostication, and Workflow.
AU - Tamaghna Ghosh
PY - 2026
DA - 2026/10/01
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1829088
UR - https://europepmc.org/article/MED/42921641
AB - Artificial intelligence (AI) is reshaping neuroimaging across the entirety of the clinical workflow, encompassing patient scheduling, scan quality assurance, image acquisition and reconstruction, automated lesion detection, pathological characterization, treatment planning, and outcome prognostication. This review synthesizes contemporary evidence through a workflow-centric framework, with particular emphasis on AI tools that have achieved real-world clinical implementation. Although reported technical performance across these domains is frequently compelling, critical deficiencies persist in external validation, human-AI benchmarking, model calibration, and standardized performance reporting, collectively impeding full clinical integration. Emerging evidence suggests that the role of the neuroradiologist is transitioning toward a collaborative human-AI paradigm, rather than one of displacement.
SN - 0971-3026
SN - 1998-3808
ER -