Pathology Foundation Models

• URL: http://arxiv.org/abs/2407.21317v2
• Date: Tue, 6 Aug 2024 05:42:42 GMT
• Title: Pathology Foundation Models
• Authors: Mieko Ochi, Daisuke Komura, Shumpei Ishikawa,
• Abstract summary: Development of deep learning technologies have led to extensive research and development in pathology AI (Artificial Intelligence) Large-scale AI models known as Foundation Models (FMs) have emerged and expanded their application scope in the healthcare field. FMs are more accurate and applicable to a wide range of tasks compared to traditional AI.
• Score: 0.0354287905099182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pathology has played a crucial role in the diagnosis and evaluation of patient tissue samples obtained from surgeries and biopsies for many years. The advent of Whole Slide Scanners and the development of deep learning technologies have significantly advanced the field, leading to extensive research and development in pathology AI (Artificial Intelligence). These advancements have contributed to reducing the workload of pathologists and supporting decision-making in treatment plans. Recently, large-scale AI models known as Foundation Models (FMs), which are more accurate and applicable to a wide range of tasks compared to traditional AI, have emerged, and expanded their application scope in the healthcare field. Numerous FMs have been developed in pathology, and there are reported cases of their application in various tasks, such as disease diagnosis, rare cancer diagnosis, patient survival prognosis prediction, biomarker expression prediction, and the scoring of immunohistochemical expression intensity. However, several challenges remain for the clinical application of FMs, which healthcare professionals, as users, must be aware of. Research is ongoing to address these challenges. In the future, it is expected that the development of Generalist Medical AI, which integrates pathology FMs with FMs from other medical domains, will progress, leading to the effective utilization of AI in real clinical settings to promote precision and personalized medicine.

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