Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis

• Published in: Medical science monitor Vol. 30; p. e947038
• Main Author: Parums, Dinah V.
• Format: Journal Article
• Language: English
• Published: United States International Scientific Literature, Inc 01.11.2024
• Subjects: Algorithms; Artificial Intelligence - trends; Diagnostic Imaging - methods; Diagnostic Imaging - trends; Humans; Image Processing, Computer-Assisted - methods; Machine Learning - trends; Neoplasms - diagnosis; Neoplasms - diagnostic imaging; Neural Networks, Computer; Prognosis
• ISSN: 1643-3750; 1234-1010; 1643-3750
• DOI: 10.12659/MSM.947038

On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics was awarded to Hopfield and Hinton for their foundation research on machine learning with artificial neural networks, which resulted in the current applications for artificial intelligence (AI). Digital diagnostic histopathology combines image capture with image analysis and uses digital tools to collect, analyze, and share diagnostic information. An increase in chronic diseases, diagnostic departmental workloads, and diagnostic tests to support targeted therapy in cancer patients have driven the use and development of image analysis systems, and several medical device companies have recently developed whole-slide scanning devices. In April 2017, the US Food and Drug Administration (FDA) permitted marketing authorization for the first whole slide imaging (WSI) system. During 2024, large-scale studies from several cancer centers have shown the potential for diagnostic reporting for real-world data and whole-slide modeling to develop validated diagnostic AI algorithms. This editorial discusses why recent advances and applications in AI and digital image analysis may have an important future role in cancer diagnosis and prognosis.