Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| author | Zerbe, Norman Schwen, Lars Ole Geißler, Christian Wiesemann, Katja Bisson, Tom Boor, Peter Carvalho, Rita Franz, Michael Jansen, Christoph Kiehl, Tim-Rasmus Lindequist, Björn Pohlan, Nora Charlotte Schmell, Sarah Strohmenger, Klaus Zakrzewski, Falk Plass, Markus Takla, Michael Küster, Tobias Homeyer, André Hufnagl, Peter |
| author_facet | Zerbe, Norman Schwen, Lars Ole Geißler, Christian Wiesemann, Katja Bisson, Tom Boor, Peter Carvalho, Rita Franz, Michael Jansen, Christoph Kiehl, Tim-Rasmus Lindequist, Björn Pohlan, Nora Charlotte Schmell, Sarah Strohmenger, Klaus Zakrzewski, Falk Plass, Markus Takla, Michael Küster, Tobias Homeyer, André Hufnagl, Peter |
| contents | Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09450 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative Zerbe, Norman Schwen, Lars Ole Geißler, Christian Wiesemann, Katja Bisson, Tom Boor, Peter Carvalho, Rita Franz, Michael Jansen, Christoph Kiehl, Tim-Rasmus Lindequist, Björn Pohlan, Nora Charlotte Schmell, Sarah Strohmenger, Klaus Zakrzewski, Falk Plass, Markus Takla, Michael Küster, Tobias Homeyer, André Hufnagl, Peter Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future. |
| title | Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative |
| topic | Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2401.09450 |