A perspective for adapting generalist AI to specialized medical AI applications and their challenges
Abstract We introduce a framework to adapt large language models for medicine: (1) Modeling: breaking down medical workflows into manageable steps; (2) Optimization: optimizing model performance via advanced adaptations; and (3) System engineering: developing agent or chain systems. Furthermore, we...
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| Main Authors: | , , , , , , , , , , , |
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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/s41746-025-01789-7 |
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| _version_ | 1849761123036823552 |
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| author | Zifeng Wang Hanyin Wang Benjamin Danek Ying Li Christina Mack Luk Arbuckle Devyani Biswal Hoifung Poon Yajuan Wang Pranav Rajpurkar Cao Xiao Jimeng Sun |
| author_facet | Zifeng Wang Hanyin Wang Benjamin Danek Ying Li Christina Mack Luk Arbuckle Devyani Biswal Hoifung Poon Yajuan Wang Pranav Rajpurkar Cao Xiao Jimeng Sun |
| author_sort | Zifeng Wang |
| collection | DOAJ |
| description | Abstract We introduce a framework to adapt large language models for medicine: (1) Modeling: breaking down medical workflows into manageable steps; (2) Optimization: optimizing model performance via advanced adaptations; and (3) System engineering: developing agent or chain systems. Furthermore, we describe varied use cases, such as clinical trial design, clinical decision support, and medical imaging analysis. Finally, we discuss challenges and considerations for building medical AI with LLMs. |
| format | Article |
| id | doaj-art-8f99fb2f3b4c48e59754b7c9671244e2 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-8f99fb2f3b4c48e59754b7c9671244e22025-08-20T03:06:08ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111310.1038/s41746-025-01789-7A perspective for adapting generalist AI to specialized medical AI applications and their challengesZifeng Wang0Hanyin Wang1Benjamin Danek2Ying Li3Christina Mack4Luk Arbuckle5Devyani Biswal6Hoifung Poon7Yajuan Wang8Pranav Rajpurkar9Cao Xiao10Jimeng Sun11 Keiji AIDepartment of Computer Science, University of Illinois Urbana-Champaign Keiji AIRegeneronIQVIAIQVIAIQVIAMicrosoft ResearchTeladoc HealthDepartment of Biomedical Informatics, Harvard Medical SchoolGE Healthcare Keiji AIAbstract We introduce a framework to adapt large language models for medicine: (1) Modeling: breaking down medical workflows into manageable steps; (2) Optimization: optimizing model performance via advanced adaptations; and (3) System engineering: developing agent or chain systems. Furthermore, we describe varied use cases, such as clinical trial design, clinical decision support, and medical imaging analysis. Finally, we discuss challenges and considerations for building medical AI with LLMs.https://doi.org/10.1038/s41746-025-01789-7 |
| spellingShingle | Zifeng Wang Hanyin Wang Benjamin Danek Ying Li Christina Mack Luk Arbuckle Devyani Biswal Hoifung Poon Yajuan Wang Pranav Rajpurkar Cao Xiao Jimeng Sun A perspective for adapting generalist AI to specialized medical AI applications and their challenges npj Digital Medicine |
| title | A perspective for adapting generalist AI to specialized medical AI applications and their challenges |
| title_full | A perspective for adapting generalist AI to specialized medical AI applications and their challenges |
| title_fullStr | A perspective for adapting generalist AI to specialized medical AI applications and their challenges |
| title_full_unstemmed | A perspective for adapting generalist AI to specialized medical AI applications and their challenges |
| title_short | A perspective for adapting generalist AI to specialized medical AI applications and their challenges |
| title_sort | perspective for adapting generalist ai to specialized medical ai applications and their challenges |
| url | https://doi.org/10.1038/s41746-025-01789-7 |
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