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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.10157 |
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| _version_ | 1866908678083313664 |
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| author | Li, Michelle M. Reis, Ben Y. Rodman, Adam Cai, Tianxi Dagan, Noa Balicer, Ran D. Loscalzo, Joseph Kohane, Isaac S. Zitnik, Marinka |
| author_facet | Li, Michelle M. Reis, Ben Y. Rodman, Adam Cai, Tianxi Dagan, Noa Balicer, Ran D. Loscalzo, Joseph Kohane, Isaac S. Zitnik, Marinka |
| contents | Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Their adaptation to new populations, specialties, or care settings often relies on fine-tuning, prompting, or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors: outputs that appear plausible but miss critical patient or situational information. We envision context switching as a solution. Context switching adjusts model reasoning at inference without retraining. Generative models can tailor outputs to patient biology, care setting, or disease. Multimodal models can reason on notes, laboratory results, imaging, and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on tasks and users. In each case, context switching enables medical AI to adapt across specialties, populations, and geographies. It requires advances in data design, model architectures, and evaluation frameworks, and establishes a foundation for medical AI that scales to infinitely many contexts while remaining reliable and suited to real-world care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence Li, Michelle M. Reis, Ben Y. Rodman, Adam Cai, Tianxi Dagan, Noa Balicer, Ran D. Loscalzo, Joseph Kohane, Isaac S. Zitnik, Marinka Artificial Intelligence Computation and Language Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Their adaptation to new populations, specialties, or care settings often relies on fine-tuning, prompting, or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors: outputs that appear plausible but miss critical patient or situational information. We envision context switching as a solution. Context switching adjusts model reasoning at inference without retraining. Generative models can tailor outputs to patient biology, care setting, or disease. Multimodal models can reason on notes, laboratory results, imaging, and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on tasks and users. In each case, context switching enables medical AI to adapt across specialties, populations, and geographies. It requires advances in data design, model architectures, and evaluation frameworks, and establishes a foundation for medical AI that scales to infinitely many contexts while remaining reliable and suited to real-world care. |
| title | One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.10157 |