"Artificial histology" in colonic Neoplasia: A critical approach.

Background: The histological assessment of colorectal precancer and cancer lesions is challenging and primarily impacts the clinical strategies of secondary colon cancer prevention. Artificial intelligence (AI) models may potentially assist in the histological diagnosis of this spectrum of phenotypical changes.

Objectives: To provide a current overview of the evidence on AI-based methods for histologically assessing colonic precancer and cancer lesions.

Methods: Based on the available studies, this review focuses on the reliability of AI-driven models in ranking the histological phenotypes included in colonic oncogenesis.

Results: This review acknowledges the efforts to shift from subjective pathologists-based to more objective AI-based histological phenotyping. However, it also points out significant limitations and areas that require improvement.

Conclusions: Current AI-driven methods have not yet achieved the expected level of clinical effectiveness, and there are still significant ethical concerns that need careful consideration. The integration of "artificial histology" into diagnostic practice requires further efforts to combine advancements in engineering techniques with the expertise of pathologists.