Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment

Artificial intelligence (AI) is transforming histologic assessment, evolving from a diagnostic adjunct to an integral component of clinical decision-making. Over the past decade, AI applications have significantly advanced histopathology, facilitating tasks from tissue classification to predicting cancer prognosis, gene alterations, and therapy responses. These developments are supported by the availability of high-quality whole-slide images (WSIs) and publicly accessible databases like The Cancer Genome Atlas (TCGA), which integrate histologic, genomic, and clinical data. Deep learning techniques replicate and enhance pathologists' decisions, addressing challenges such as inter-observer variability and diagnostic reproducibility. Moreover, AI enables robust predictions of patient prognosis, actionable gene statuses, and therapy responses, offering rapid, cost-effective alternatives to conventional methods. Innovations such as histomorphologic phenotype clusters and spatial transcriptomics have further refined cancer stratification and treatment personalization. In addition, multimodal approaches integrating histologic images with clinical and molecular data have achieved superior predictive accuracy and explainability. Nevertheless, challenges remain in verifying AI predictions, particularly for prognostic applications and ensuring accessibility in resource-limited settings. Addressing these challenges will require standardized datasets, ethical frameworks, and scalable infrastructure. While AI is revolutionizing histologic assessment for cancer diagnosis and treatment, optimizing digital infrastructure and long-term strategies is essential for its widespread adoption in clinical practice.