Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)
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arXiv
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| Format: | Preprint |
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2025
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| author | Fang, Zhongnan Johnston, Andrew Cheuy, Lina Na, Hye Sun Paschali, Magdalini Gonzalez, Camila Armstrong, Bonnie A. Koirala, Arogya Laurel, Derrick Campion, Andrew Walker Iv, Michael Chaudhari, Akshay S. Larson, David B. |
| author_facet | Fang, Zhongnan Johnston, Andrew Cheuy, Lina Na, Hye Sun Paschali, Magdalini Gonzalez, Camila Armstrong, Bonnie A. Koirala, Arogya Laurel, Derrick Campion, Andrew Walker Iv, Michael Chaudhari, Akshay S. Larson, David B. |
| contents | Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM successfully categorizes confidence in the AI-generated prediction, suggesting different actions and helping improve the overall performance of AI tools to ultimately reduce cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM) Fang, Zhongnan Johnston, Andrew Cheuy, Lina Na, Hye Sun Paschali, Magdalini Gonzalez, Camila Armstrong, Bonnie A. Koirala, Arogya Laurel, Derrick Campion, Andrew Walker Iv, Michael Chaudhari, Akshay S. Larson, David B. Artificial Intelligence Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM successfully categorizes confidence in the AI-generated prediction, suggesting different actions and helping improve the overall performance of AI tools to ultimately reduce cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings. |
| title | Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM) |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.11738 |