Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond

• Published in: Advances in cancer research Vol. 161; pp. 431 - 478
• Main Authors: Chang, Justin, Hatfield, Bryce
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 2024
• Subjects: AI applications; AI challenges; Artificial Intelligence; Computer vision; Computer-aided diagnosis; Convolutional neural networks; Deep learning; Digital pathology; Humans; Image Processing, Computer-Assisted - methods; Machine Learning; Precision medicine; Precision Medicine - methods; Deep learning; AI challenges; Digital pathology; Computer vision; Precision medicine; Machine learning; AI applications; Convolutional neural networks; Artificial intelligence; Computer-aided diagnosis
• ISSN: 0065-230X; 2162-5557; 2162-5557
• DOI: 10.1016/bs.acr.2024.05.006

The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field’s evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists’ diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists’ workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.