Evidence map›Paper›PMID 39632098›Full record

ArticleBMJ health & care informatics2024

Strategies for creation of data reserve and stress testing of medical AI products.

Huai Chen, Yanmei Tie, Xinhua Cao, Geoffrey S Young, Xiaoyin Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ health & care informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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.

Huai Chen *Department of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Yanmei Tie *Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Xinhua CaoDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Geoffrey S YoungDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Xiaoyin XuKey Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China xxu@ieee.org.ORCID http://orcid.org/0000-0003-0813-7979

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the fast development of artificial intelligence (AI) and its applications in medicine, it is often said that the time for intelligent medicine is arriving, if not already have arrived. While there is no doubt that AI-centred intelligent medicine will transform current healthcare, it is necessary to test and re-test medical AI (MAI) products before they are implemented in the real world. From the perspective of ensuring safety, accuracy and efficiency, it is imperative that MAIs undergo stress tests in a systematic and comprehensive manner where stress tests subject MAIs to workloads and environments beyond tests carried out by their developers. In such stress tests, potential bottlenecks or failures of MAIs may be identified and fed back to developers to optimise the products. To avoid bias and ensure fairness, stress tests should be prepared and administered by an independent body.

Indexed as

Artificial IntelligenceExercise TestHumansArtificial intelligenceDeep LearningMachine LearningSoftware

Identifiers

PMID39632098
PMCPMC11624794

What OpenQuestion holds

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Registered trials

None linked

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.