Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis

Artificial intelligence (AI) is an important branch of computer science. With the rapid development of AI, the application in oncology has become increasingly widespread. As a hematologic malignancy characterized by remarkable heterogeneity, lymphoma has long posed significant challenges in both diagnosis and treatment, particularly with regard to its complex classification and difficulties in prognostic evaluation. Breakthroughs in AI technology have provided a new paradigm for precision treatment of tumors. AI can integrate and analyze HE pathology slides and PET/CT images to enhance diagnostic efficiency; meanwhile, in terms of treatment prognosis, AI can identify biomarkers to accurately classify lymphoma subtypes for therapeutic guidance, simultaneously quantify biomarkers to minimize the influence of subjective variability, and predict the patients' prognosis based on the extracted features to assist in the precise treatment of lymphoma. This review aims to provide an overview of AI related to various fields of lymphoma, introducing the core technologies and principles of AI, including deep learning, decision trees, regression models, and so on. We also discuss the clinical applications of AI in lymphoma pathology slides and PET/CT images, systematically analyze the clinical applications of AI in diagnosis, treatment, and prognosis, as well as innovatively summarize the cutting-edge applications of AI in 3D pathology of lymphomas, and finally, we emphasize the development potentials and current challenges of AI in the field of lymphomas to promote precision lymphoma diagnosis and treatment by providing a theoretical foundation.