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Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020–2025)

Crossref DOI link: https://doi.org/10.1007/s10877-025-01404-w

Published Online: 2026-01-31

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Chinn, Lawrence Willis

Nemeh, Isabelle

Chinn, Natasha R.
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Text and Data Mining valid from 2026-01-31

Version of Record valid from 2026-01-31
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Article History

Received: 20 September 2025

Accepted: 17 December 2025

First Online: 31 January 2026

Declarations

:

: Not applicable.

: The authors declare they have no financial interests related to this work. Non-financial interests: Natasha R. Chinn, MD, FACOG, DipABLM, ABOIM, is President-Elect and a board member of the Greater New Jersey Chapter of the American Heart Association and serves on its executive leadership team. She is also a community member of Connecting Our Communities for NJ (COCNJ) since 2023 and currently serves as a member of its Board of Trustees (2025–present). In addition, she is a member of the Community Advisory Committee for the New Jersey Birth Equity Funders Alliance (NJBEFA) since 2023. Lawrence Willis Chinn, MD, and Isabelle Nemeh, MD, have no non-financial interests to disclose.

: Not applicable. This article is a scoping review and does not contain any studies with human participants or animals performed by any of the authors.

: Not applicable.

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