ArticleHuman genomics2024
Analysis of public perceptions on the use of artificial intelligence in genomic medicine.
Article in Human genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Automating genomic reanalysis: perspectives of people living with, or impacted by, a genetic, rare or undiagnosed condition.BMC medical ethics · 2026Article
- Genomics workforce views on automating genomic reanalysis: trust, equity and governance.Human genetics · 2026Article
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- Public hesitancy for AI-based detection of neurodegenerative diseases in France.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNext generation sequencing has led to the creation of large pools of genomic data with analysis rather than data generation now the limiting factor. Artificial intelligence (AI) may be required to optimize the benefits of these data, but little is known about how the public feels about the use of AI in genomics.
methodsWe conducted focus groups with members of the Australian public. Participants were recruited via social media advertisements. We explored potential uses of AI in genomic medicine, the benefits, risks, and the possible social implications of its use.
resultsParticipants (n = 34) largely felt comfortable with AI analysing their own genomic data and generally agreed about its benefits. Concerns were raised over data security, the potential for misdiagnosis, and bias AI may perpetuate. Many participants wanted checking mechanisms for when results were generated using AI.
conclusionsThe insights gained from these discussions help to understand public concerns around the use of AI in genomic medicine. Our findings can help to inform both policies around genomic AI and how to educate the public on its use.
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What OpenQuestion holds
Registered trials
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.