The increasing cancer incidence is causing a significant burden and diagnostic delays at pathology laboratories. This project aims to develop and validate artificial intelligence (AI) in computational pathology for enhanced cancer diagnostics. Several thousand unidentified whole slide images of primary tumors and metastases will be collected to develop and test AI algorithms. Integrating AI into digital pathology has the potential to reduce the workload on pathologists, minimize inter-observer variability, uncover novel histopathological features, and enhance diagnostic accuracy. The integration of AI in digital pathology holds the potential to alleviate the workload and shorten the diagnostic delays. The use of AI could reveal novel histopathological features not seen by the naked eye such as genetic changes and prognostic factors. Our hypothesis posits that AI could predict the likelihood of metastatic disease from primary tumors, which would revolutionize cancer diagnostics and enable early adjuvant treatment for patients even before metastases manifest. Due to the high costs of molecular genetic analyses, a large portion of patients are not routinely profiled. The development of alternative screening tools capable of detecting mutations quickly and cheaply on digitalised routine stained pathology slides could save costs and enable personalized cancer care for wider patient population.<\/p>\n","protected":false},"menu_order":0,"template":"","class_list":["post-5058","tutkimus","type-tutkimus","status-publish","hentry"],"acf":[],"yoast_head":"\n