In Journal of the National Cancer Institute
Pathologists worldwide are facing remarkable challenges with the increasing workloads and the lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole slide images has the potential of democratizing the access to expert pathology and affordable biomarkers, by supporting pathologists in the provision of timely and accurate diagnosis as well as supporting oncologists by extracting prognostic and predictive biomarkers directly from tissue slides. The long-awaited adoption of AI in pathology, however, has not materialized, and the transformation of pathology is happening at a pace that is much slower than that observed in other fields (eg,, radiology). Here, we provide a critical summary of the developments in digital and computational pathology in the last ten years, outline key hurdles and ways to overcome them, and provide a perspective for AI-supported precision oncology in the future.
Reis-Filho Jorge S, Kather Jakob Nikolas
2023-Mar-17
Artificial intelligence, deep learning, machine learning, oncology, pathology
