<p>The intention-execution disconnect in Medical AI: The reXecution framework for evaluating real-world clinical performance</p>

Oishi Banerjee; Lucas Bijnens; Subathra Adithan; Pranav Rajpurkar

doi:10.7490/f1000research.1120371.1

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Banerjee O, Bijnens L, Adithan S and Rajpurkar P. The intention-execution disconnect in Medical AI: The reXecution framework for evaluating real-world clinical performance [version 1; not peer reviewed]. F1000Research 2025, 14:1122 (poster) (https://doi.org/10.7490/f1000research.1120371.1)

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The intention-execution disconnect in Medical AI: The reXecution framework for evaluating real-world clinical performance

Oishi Banerjee¹, Lucas Bijnens, Subathra Adithan, Pranav Rajpurkar

Published 15 Oct 2025 (https://doi.org/10.7490/f1000research.1120371.1)

Author Affiliations

¹ Biomedical Informatics, Harvard Medical School Department of Biomedical Informatics, Boston, Massachusetts, USA

Copyright © 2025 Banerjee O et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 15 Oct 2025

The intention-execution disconnect in Medical AI: The reXecution framework for evaluating real-world clinical performance
[version 1; not peer reviewed]

Oishi Banerjee¹, Lucas Bijnens, Subathra Adithan, Pranav Rajpurkar

Author Affiliations

¹ Biomedical Informatics, Harvard Medical School Department of Biomedical Informatics, Boston, Massachusetts, USA

Abstract

Competing Interests

No competing interests were disclosed

Keywords

AI, medicine, radiology, human-AI collaboration

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