Evidence map›Paper›PMID 35087178›Full record

ArticleNPJ digital medicine2022

Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study.

Shruti Jayakumar, Viknesh Sounderajah, Pasha Normahani, Leanne Harling, Sheraz R Markar, Hutan Ashrafian, Ara Darzi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 9 pooled it
–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

54 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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  6. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
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  14. Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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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

7 authors.

Shruti Jayakumar *Department of Surgery and Cancer, Imperial College London, London, UK.
Viknesh Sounderajah *Department of Surgery and Cancer, Imperial College London, London, UK.
Pasha NormahaniDepartment of Surgery and Cancer, Imperial College London, London, UK.
Leanne HarlingDepartment of Surgery and Cancer, Imperial College London, London, UK.
Sheraz R MarkarDepartment of Surgery and Cancer, Imperial College London, London, UK.
Hutan AshrafianDepartment of Surgery and Cancer, Imperial College London, London, UK. hutan@researchtrials.net.ORCID http://orcid.org/0000-0003-1668-0672
Ara DarziDepartment of Surgery and Cancer, Imperial College London, London, UK.ORCID http://orcid.org/0000-0001-7815-7989

Funding

Medical Research Council MC_PC_18032
6 · The paper itself

Abstract

Artificial intelligence (AI) centred diagnostic systems are increasingly recognised as robust solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in secondary research studies regarding these technologies in order to influence key clinical and policymaking decisions. It is therefore essential that these studies accurately appraise methodological quality and risk of bias within shortlisted trials and reports. In order to assess whether this critical step is performed, we undertook a meta-research study evaluating adherence to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool within AI diagnostic accuracy systematic reviews. A literature search was conducted on all studies published from 2000 to December 2020. Of 50 included reviews, 36 performed the quality assessment, of which 27 utilised the QUADAS-2 tool. Bias was reported across all four domains of QUADAS-2. Two hundred forty-three of 423 studies (57.5%) across all systematic reviews utilising QUADAS-2 reported a high or unclear risk of bias in the patient selection domain, 110 (26%) reported a high or unclear risk of bias in the index test domain, 121 (28.6%) in the reference standard domain and 157 (37.1%) in the flow and timing domain. This study demonstrates the incomplete uptake of quality assessment tools in reviews of AI-based diagnostic accuracy studies and highlights inconsistent reporting across all domains of quality assessment. Poor standards of reporting act as barriers to clinical implementation. The creation of an AI-specific extension for quality assessment tools of diagnostic accuracy AI studies may facilitate the safe translation of AI tools into clinical practice.

Identifiers

PMID35087178
PMCPMC8795185

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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.