Unmasking and quantifying racial bias of large language models in medical report generation
Abstract Background Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and ext...
Furkejuvvon:
| Váldodahkkit: | , , , , |
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| Materiálatiipa: | Artigo |
| Giella: | Inglês |
| Almmustuhtton: |
Nature Portfolio
2024-09-01
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| Ráidu: | Communications Medicine |
| Liŋkkat: | https://doi.org/10.1038/s43856-024-00601-z |
| Fáddágilkorat: |
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