Ana Luísa Carvalho
Universidade de São Paulo, Brazil
ABSTRACT
Starting to apply this new technology for primary diagnosis was made possible by the FDA's recent approval of the prostate AI algorithm and WSI scanners for use in primary diagnosis. Workflow breakthroughs and advancements in anatomical and clinical pathology can be made possible by AI tools. With a focus on the future, we outline the significant studies and turning points in the application of AI in clinical pathology in this study. Many pathologists expect artificial intelligence (AI) to help them with a range of digital pathology tasks, given AI's recent success in computer vision applications. Concurrently, remarkable developments in deep learning have made it possible for AI and deep learning to work in concert, enabling image-based diagnosis against the backdrop of digital pathology. AI-based solutions are being developed to reduce errors and save pathologist’s time. Here, we outline the components of computational pathology (CPATH), how it might be applied to the development of AI, and the difficulties it encounters with regard to computing systems, reimbursement, ethics, and legal issues, as well as algorithm validation and interpretability. Additionally, we provide a summary of cutting-edge AI-based methods that could be incorporated into workflows in pathology labs.
Keywords: Artificial Intelligence (AI), Histopathological Diagnosis (HD), Pathology (PP), Technology (T).
