Evidence map›Paper›PMID 41784829›Full record

ArticleHuman genetics2026

Genomics workforce views on automating genomic reanalysis: trust, equity and governance.

Emily A King, Fiona Lynch, Zornitza Stark, Danya F Vears

Abstract read
In one paragraph

Article in Human genetics, 2026. 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

4 authors.

Emily A KingMurdoch Children's Research Institute, Parkville, VIC, 3052, Australia.
Fiona LynchMurdoch Children's Research Institute, Parkville, VIC, 3052, Australia.
Zornitza StarkDepartment of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia.
Danya F VearsMurdoch Children's Research Institute, Parkville, VIC, 3052, Australia. danya.vears@mcri.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automation of genomic data reanalysis can increase diagnostic rates in a timely and cost-effective manner. However, implementing automated reanalysis brings with it both ethical and practical challenges. We sought to explore how clinical and laboratory staff view these challenges as part of a broader study piloting automated genomic reanalysis. Focus groups were conducted with 21 genetics health professionals recruited from a previous survey and purposively sampled from clinical genetics diagnostics services and laboratories. Participants were supportive of automating reanalysis but discussed trust and accuracy, and emphasized the need for transparency, updated phenotypic information and human checking in an automated model. Legal versus moral obligation to return results when patients are unable to be recontacted was extensively discussed. Participants held concerns about a potential legal obligation to return results arising as reanalysis becomes routine and potential for impacts on workflow. A centralized system with national governance was proposed to ensure development and implementation of an equitable service and facilitate communication between patients and genetics services. However, this may be limited by existing infrastructure and the public’s perception of government services. In conclusion, participants in this study supported automated reanalysis, however issues regarding transparency, trust and accuracy will need to be systematically addressed to support implementation.

Indexed as

GenomicsAutomationFocus GroupsHumansReturn of Individual Research ResultsTrust

Identifiers

PMID41784829
PMCPMC12963070

What OpenQuestion holds

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LicenceCC BY
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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.