Cognitive bias in clinical large language models
Cognitive bias accounts for a significant portion of preventable errors in healthcare, contributing to significant patient morbidity and mortality each year. As large language models (LLMs) are introduced into healthcare and clinical decision-making, these systems are at risk of inheriting – and eve...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01790-0 |
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| _version_ | 1849761121663188992 |
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| author | Arjun Mahajan Ziad Obermeyer Roxana Daneshjou Jenna Lester Dylan Powell |
| author_facet | Arjun Mahajan Ziad Obermeyer Roxana Daneshjou Jenna Lester Dylan Powell |
| author_sort | Arjun Mahajan |
| collection | DOAJ |
| description | Cognitive bias accounts for a significant portion of preventable errors in healthcare, contributing to significant patient morbidity and mortality each year. As large language models (LLMs) are introduced into healthcare and clinical decision-making, these systems are at risk of inheriting – and even amplifying – these existing biases. This article explores both the cognitive biases impacting LLM-assisted medicine and the countervailing strengths these technologies bring to addressing these limitations. |
| format | Article |
| id | doaj-art-89fc20c7c9f04e4489e3825181b4fa4c |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-89fc20c7c9f04e4489e3825181b4fa4c2025-08-20T03:06:08ZengNature Portfolionpj Digital Medicine2398-63522025-07-01811410.1038/s41746-025-01790-0Cognitive bias in clinical large language modelsArjun Mahajan0Ziad Obermeyer1Roxana Daneshjou2Jenna Lester3Dylan Powell4Harvard Medical SchoolSchool of Public Health, University of California, BerkeleyDepartment of Biomedical Data Science, Stanford UniversityDepartment of Dermatology, University of California, San FranciscoFaculty of Health Sciences & Sport, University of StirlingCognitive bias accounts for a significant portion of preventable errors in healthcare, contributing to significant patient morbidity and mortality each year. As large language models (LLMs) are introduced into healthcare and clinical decision-making, these systems are at risk of inheriting – and even amplifying – these existing biases. This article explores both the cognitive biases impacting LLM-assisted medicine and the countervailing strengths these technologies bring to addressing these limitations.https://doi.org/10.1038/s41746-025-01790-0 |
| spellingShingle | Arjun Mahajan Ziad Obermeyer Roxana Daneshjou Jenna Lester Dylan Powell Cognitive bias in clinical large language models npj Digital Medicine |
| title | Cognitive bias in clinical large language models |
| title_full | Cognitive bias in clinical large language models |
| title_fullStr | Cognitive bias in clinical large language models |
| title_full_unstemmed | Cognitive bias in clinical large language models |
| title_short | Cognitive bias in clinical large language models |
| title_sort | cognitive bias in clinical large language models |
| url | https://doi.org/10.1038/s41746-025-01790-0 |
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