Integration of artificial intelligence in histopathological and radiological image analysis

Aim: This review explores the integration of artificial intelligence (AI) in both histopathological and radiological image analysis, focusing on its potential to enhance diagnostic workflows and patient outcomes. Methods: We examined recent advancements in AI technologies, particularly deep learning and computational pathology (CPath), highlighting methodologies such as multiple instance learning (MIL) and graph neural networks (GNNs) for analyzing whole slide images (WSIs) and radiological imaging techniques like MRI and CT scans. The review also discusses challenges in data privacy, ethical concerns, and regulatory needs. Results: AI-driven tools have demonstrated improved accuracy in detecting diseases such as cancers by automating image analysis and enhancing image quality. Techniques like virtual staining and segmentation facilitate the quantification of morphological traits, enabling better prognostic predictions. Radiological imaging techniques integrated with AI provide crucial complementary information on anatomical abnormalities and disease progression. Despite these advancements, challenges like the need for substantial human annotation and computational resources persist. Conclusion: The future of AI in histopathology and radiology looks promising, with ongoing innovations poised to refine diagnostic capabilities and foster personalized medicine. Addressing ethical and practical concerns will be critical for the responsible implementation of AI technologies in clinical settings.