Artificial intelligence in neuro-oncology: Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues

• Pedro David Delgado-López ^[1] ; Miguel Cárdenas Montes ^[2] ; Jesús Troya García ^[3] ; Beatriz Ocaña-Tienda ^[4] ; Santiago Cepeda ^[5] ; Ricard Martínez Martínez ^[6] ; Eva María Corrales-García ^[1]
  1. [1] Complejo Asistencial Universitario de Burgos

     Complejo Asistencial Universitario de Burgos

     Burgos, España

     
  2. [2] Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas

     Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas

     Madrid, España

     
  3. [3] Hospital Infanta Leonor

     Hospital Infanta Leonor

     Madrid, España

     
  4. [4] Centro Nacional de Investigaciones Oncológicas

     Centro Nacional de Investigaciones Oncológicas

     Madrid, España

     
  5. [5] Hospital Universitario Pío del Río Hortega

     Hospital Universitario Pío del Río Hortega

     Valladolid, España

     
  6. [6] Universitat de València

     Universitat de València

     Valencia, España

     

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• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 27, Nº. 11, 2025, págs. 4117-4130
• Idioma: inglés
• DOI: 10.1007/s12094-025-03948-4
• Texto completo no disponible (Saber más ...)
• Resumen

  • Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches—such as CNNs and deep learning—have improved the detection and classification of brain tumors through advanced imaging techniques and genomic analysis. Explainable AI methods mitigate the “black box” problem, promoting model transparency and clinical trust. Mechanistic models complement AI by integrating biological principles, enabling precise tumor growth predictions and treatment response assessments. AI applications also include the creation of digital twins for personalized therapy optimization, virtual clinical trials, and predictive modeling for estimation of tumor resection and pattern of recurrence. However, challenges such as data bias, ethical concerns, and regulatory compliance persist. The European Artificial Intelligence Act and the Health Data Space Regulation impose strict data protection and transparency requirements. This review explores AI’s methodological foundations, clinical applications, and ethical challenges in neuro-oncology, emphasizing the need for interdisciplinary collaboration and regulatory adaptation.