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Response to correspondence regarding “Analysis of large-language model versus human performance for genetics questions”

European Journal of Human Genetics (2023)Cite this article

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References

  1. Duong D, Solomon BD. Analysis of large-language model versus human performance for genetics questions. Eur J Hum Genet. 2023. https://doi.org/10.1038/s41431-023-01396-8. Epub ahead of print.

  2. Emmert-Streib F. Can ChatGPT understand genetics? Eur J Hum Genet. 2023. https://doi.org/10.1038/s41431-023-01419-4. Epub ahead of print.

  3. Singhal K, Azizi S, Tu T, Mahdavi S, Wei J, Won Chung H, et al. Large language models encode clinical knowledge. arXiv preprint. arXiv:221213138. 2022.

  4. Solomon BD. Medical genetics and genomics: questions for board review. Hoboken: Wiley; 2022.

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Funding

This work was supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health.

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Authors and Affiliations

  1. Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research Institute, Bethesda, MD, USA

    Dat Duong & Benjamin D. Solomon

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  1. Dat Duong
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  2. Benjamin D. Solomon
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Correspondence to Benjamin D. Solomon.

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Competing interests

The authors receive salary and research support from the intramural program of the National Human Genome Research Institute. Benjamin D. Solomon is the co-Editor-in-Chief of the American Journal of Medical Genetics, and published some of the questions mentioned in the original study in a book, as well as others [4]. Both editing/publishing activities are conducted as an approved outside activity, separate from his US Government role.

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Cite this article

Duong, D., Solomon, B.D. Response to correspondence regarding “Analysis of large-language model versus human performance for genetics questions”. Eur J Hum Genet (2023). https://doi.org/10.1038/s41431-023-01444-3

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  • DOI: https://doi.org/10.1038/s41431-023-01444-3

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