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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.04187 |
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| _version_ | 1866912748199215104 |
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| author | Hu, Jinzhen Faust, Kevin Zadeh, Parsa Babaei Bourkas, Adrienn Eaton, Shane Young, Andrew Alvi, Anzar Oreopoulos, Dimitrios George Paliwal, Ameesha Alrumeh, Assem Saleh Kamski-Hennekam, Evelyn Rose Diamandis, Phedias |
| author_facet | Hu, Jinzhen Faust, Kevin Zadeh, Parsa Babaei Bourkas, Adrienn Eaton, Shane Young, Andrew Alvi, Anzar Oreopoulos, Dimitrios George Paliwal, Ameesha Alrumeh, Assem Saleh Kamski-Hennekam, Evelyn Rose Diamandis, Phedias |
| contents | The microscopic examination of surgical tissue remains a cornerstone of disease classification but relies on subjective interpretations and access to highly specialized experts, which can compromise accuracy and clinical care. While emerging breakthroughs in artificial intelligence (AI) offer promise for automated histological analysis, the growing number of proprietary digital pathology solutions has created barriers to real-world deployment. To address these challenges, we introduce OnSight Pathology, a platform-agnostic computer vision software that uses continuous custom screen captures to provide real-time AI inferences to users as they review digital slide images. Accessible as a single, self-contained executable file (https://onsightpathology.github.io/ ), OnSight Pathology operates locally on consumer-grade personal computers without complex software integration, enabling cost-effective and secure deployment in research and clinical workflows. Here we demonstrate the utility of OnSight Pathology using over 2,500 publicly available whole slide images across different slide viewers, as well as cases from our clinical digital pathology setup. The software's robustness is highlighted across routine histopathological tasks, including the classification of common brain tumor types, mitosis detection, and the quantification of immunohistochemical stains. A built-in multi-modal chat assistant provides verifiable descriptions of images, free of rigid class labels, for added quality control. Lastly, we show compatibility with live microscope camera feeds, including from personal smartphones, offering potential for deployment in more analog, inter-operative, and telepathology settings. Together, we highlight how OnSight Pathology can deliver real-time AI inferences across a broad range of pathology pipelines, removing key barriers to the adoption of AI tools in histopathology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OnSight Pathology: A real-time platform-agnostic computational pathology companion for histopathology Hu, Jinzhen Faust, Kevin Zadeh, Parsa Babaei Bourkas, Adrienn Eaton, Shane Young, Andrew Alvi, Anzar Oreopoulos, Dimitrios George Paliwal, Ameesha Alrumeh, Assem Saleh Kamski-Hennekam, Evelyn Rose Diamandis, Phedias Computer Vision and Pattern Recognition Artificial Intelligence The microscopic examination of surgical tissue remains a cornerstone of disease classification but relies on subjective interpretations and access to highly specialized experts, which can compromise accuracy and clinical care. While emerging breakthroughs in artificial intelligence (AI) offer promise for automated histological analysis, the growing number of proprietary digital pathology solutions has created barriers to real-world deployment. To address these challenges, we introduce OnSight Pathology, a platform-agnostic computer vision software that uses continuous custom screen captures to provide real-time AI inferences to users as they review digital slide images. Accessible as a single, self-contained executable file (https://onsightpathology.github.io/ ), OnSight Pathology operates locally on consumer-grade personal computers without complex software integration, enabling cost-effective and secure deployment in research and clinical workflows. Here we demonstrate the utility of OnSight Pathology using over 2,500 publicly available whole slide images across different slide viewers, as well as cases from our clinical digital pathology setup. The software's robustness is highlighted across routine histopathological tasks, including the classification of common brain tumor types, mitosis detection, and the quantification of immunohistochemical stains. A built-in multi-modal chat assistant provides verifiable descriptions of images, free of rigid class labels, for added quality control. Lastly, we show compatibility with live microscope camera feeds, including from personal smartphones, offering potential for deployment in more analog, inter-operative, and telepathology settings. Together, we highlight how OnSight Pathology can deliver real-time AI inferences across a broad range of pathology pipelines, removing key barriers to the adoption of AI tools in histopathology. |
| title | OnSight Pathology: A real-time platform-agnostic computational pathology companion for histopathology |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.04187 |