Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)

Abstract excerpt

<title>Abstract</title> <p>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 int...

Topics

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• Electronic Health Records
• Hospitalization
• Natural Language Processing
• Phenotype
• Radiomics and Machine Learning in Medical Imaging
• Reproducibility of Results
• Republic of Korea
• Stroke

Identifiers and source

Literature Corpus work

46fd4f55-bfd3-55fc-8ec2-69d19109f975

DOI

10.21203/rs.3.rs-6683104/v1