ArticleGenetics in medicine : official journal of the American College of Medical Genetics2026
Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeBecause clinical genetics artificial intelligence (AI) applications and capabilities are on a rapid rise, this study sought to assess workforce readiness.
methodsTo assess the workforce's use, knowledge, and attitudes about medical AI applications, we conducted a survey of 215 US-based genetics clinicians and trainees.
resultsMore than half (51.2%) of participants reported little to no knowledge of AI in clinical genetics; 64.3% reported no formal training. Formal training correlated with greater self-reported knowledge of AI in clinical genetics: 69.3% of respondents with formal training (vs 37.5% without) reported intermediate to extensive knowledge of AI. Most participants reported insufficient knowledge of clinical AI (83.4%), desired more education (97.6%), and would take available training (89.3%). The majority (51.6%) of clinician participants initially reported not using AI applications in the clinic. However, after a tutorial describing clinical AI applications, 75.8% reported some use. When asked about specific applications, the majority of clinician participants used facial diagnostic applications (54.9%) and AI-generated genomic testing results (62.1%); other applications, such as chatbots, large language models, pedigree or medical summary generators, and risk assessment, were less commonly used (11.1%-12.5%).
conclusionFurther education is both desired and needed to optimally use AI applications in clinical genetics.
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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.