Opportunities and challenges of artificial intelligence and digital pathology in adult CNS tumors

For over a century, the pathologist's interpretation at the microscope was the final word on a brain tumor's identity. The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, 5th edition, changed that hierarchy by introducing a layered, integrated diagnosis in which molecular findings take precedence over histology when the two conflict. Artificial intelligence (AI) applied to digitized slides adds a computational layer to this framework and marks a fundamental change in diagnostic workflow. This review examines the role of AI and digital pathology in adult CNS tumors and discusses the opportunities and challenges in clinical, neuro-oncology implementation.

We conducted a narrative review of relevant literature indexed in PubMed before and after the 2021 WHO classification, covering digital pathology, whole-slide imaging (WSI), and AI applications in adult neuro-oncology. We synthesized evidence on diagnostic performance, molecular prediction, prognostic modeling, workflow integration, and ethical considerations.

AI and digital pathology show potential to improve diagnostic consistency, shorten turnaround time, and integrate histopathological, molecular, and radiological information. AI models may support glioma grading, prediction of molecular alterations from routine histology, survival stratification, prediction of brain metastasis, and intraoperative decisions. Barriers to clinical adoption include limited generalizability, lack of transparency, data privacy, and evolving regulatory oversight.

AI and digital pathology are transforming adult CNS tumor diagnostics and could improve precision, efficiency, and multidisciplinary decision-making. Successful implementation will require rigorous multi-institutional clinical validation, transparent governance, equitable access, and collaboration among patients and health care professionals.