Evidence map›Paper›PMID 40852407›Full record

ArticleProceedings. IEEE International Symposium on Computer-Based Medical Systems2025

Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.

Yuansan Liu, Sudanthi Wijewickrema, Bridget Copson, Jean-Marc Gerard, Sameer Antani

Abstract read
In one paragraph

Article in Proceedings. IEEE International Symposium on Computer-Based Medical Systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuansan LiuSchool of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
Sudanthi WijewickremaDepartment of Surgery (Otolaryngology), University of Melbourne, Melbourne, Australia.
Bridget CopsonDepartment of Medical Imaging, St Vincent's Hospital, Melbourne, Australia.
Jean-Marc GerardDepartment of Surgery (Otolaryngology), University of Melbourne, Melbourne, Australia.
Sameer AntaniDivision of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

Funding

Machine learning and artificial intelligence research for clinical medical image processingZIALM010018 · NLM · NATIONAL LIBRARY OF MEDICINE · PI ANTANI, SAMEER · 2021 to 2025
$9.0M
Intramural NIH HHS Z99 LM999999Intramural NIH HHS ZIA LM010018
6 · The paper itself

Abstract

Artificial intelligence (AI) is making significant advances toward becoming a well-established and promise-bearing technology in various medical domains such as screening, diagnostics, and biopharma research. However, its state remains relatively nascent in surgery and surgical therapeutics. This presents an opportunity for leveraging ongoing rapid advances in AI technology and the increasing availability of large, diverse datasets to pave the way for their use in these domains. Expanding the use of AI to include various processes in surgery-related workflows could provide several benefits, such as greater assurance for reduced errors, better assistance to surgeons, and overall improved patient outcomes. To encourage further research in surgical AI, this article summarizes the state-of-the-art in AI assurance in various aspects of a patient's timeline when undergoing head and neck surgeries, including diagnostics, preoperative considerations, intraoperative guidance, and postoperative and outcome predictions. The work aims to highlight gaps in the state-of-the-art and identify opportunities for the computer-based medical systems community to encourage future research and development on the subject.

Indexed as

Artificial IntelligenceAssuranceHead & Neck SurgeryReliabilitySafety

Identifiers

PMID40852407
PMCPMC12369650

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.