A comprehensive review published in Intelligent Oncology (Volume 2, Issue 3) examines how AI is reshaping neuro-oncology imaging—moving the field from subjective, reader-dependent interpretation toward quantitative, reproducible decision support. Authors from Chulalongkorn University, the London School of Hygiene and Tropical Medicine, and other institutions worldwide synthesized evidence across automated detection, segmentation, grading, and longitudinal monitoring of brain tumors using multiparametric MRI and hybrid PET/MRI.

A thought-provoking editorial published in Intelligent Oncology challenges the current AI paradigm in cancer research. Authors Zejia Mao and Professor Bo Xu (Editor-in-Chief) argue that supervised learning and average-performance metrics systematically filter out rare, anomalous, and unclassifiable cases – precisely where breakthrough discoveries often hide. They propose three actionable strategies to reorient AI from mere pattern recognition toward a true engine of productive serendipity.

A study published in Intelligent Oncology presents PSIDMViT – a diffusion model that integrates physical priors, spatial priors, and a Vision Transformer – delivering Monte Carlo (MC)–level dose accuracy for CyberKnife radiotherapy in just 18 minutes per patient, a 3.5‑fold speedup over GPU‑accelerated MC.