Evidence map›Paper›PMID 38942454›Full record

ReviewKorean journal of radiology2024

Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology.

Nicole Kessa Wee, Kim-Ann Git, Wen-Jeng Lee, Gaurang Raval, Aziz Pattokhov, Evelyn Lai Ming Ho, Chamaree Chuapetcharasopon, Noriyuki Tomiyama, Kwan Hoong Ng, Cher Heng Tan

Abstract readReview
In one paragraph

Review in Korean journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Nicole Kessa WeeDepartment of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group, Singapore.ORCID 0000-0001-7247-0034
Kim-Ann GitDepartment of Diagnostic Radiology, Pantai Hospital, Kuala Lumpur, Malaysia.ORCID 0000-0002-5441-2065
Wen-Jeng LeeDepartment of Diagnostic Radiology, National Taiwan University Hospital, Taipei, Taiwan.ORCID 0000-0003-3267-4811
Gaurang RavalDepartment of Diagnostic Radiology, Workhardt Hospitals Limited, Mumbai, India.ORCID 0000-0001-6501-3134
Aziz PattokhovFaculty of Medicine, Tashkent State Dental Institute, Tashkent, Uzbekistan.ORCID 0000-0001-6570-9772
Evelyn Lai Ming HoDepartment of Diagnostic Radiology, ParkCity Medical Centre, Kuala Lumpur, Malaysia.ORCID 0000-0002-4900-610X
Chamaree ChuapetcharasoponDepartment of Diagnostic Radiology, MedPark Hospital, Bangkok, Thailand.ORCID 0009-0003-8839-1754
Noriyuki TomiyamaDepartment of Diagnostic and Interventional Radiology Suita, Osaka University Hospital, Osaka, Japan.ORCID 0000-0001-9918-7327
Kwan Hoong NgDepartment of Biomedical Imaging and University of Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia.ORCID 0000-0002-8707-914X
Cher Heng TanDepartment of Diagnostic Radiology, Tan Tock Seng Hospital, National Healthcare Group, Singapore.ORCID 0000-0003-3341-3111

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

Indexed as

Artificial IntelligenceRadiologyAsiaHumansSocieties, MedicalArtificial intelligenceAsian-Oceanian Society of RadiologyPosition statement

Identifiers

PMID38942454
PMCPMC11214917

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.