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Optimizing the input: can large language models standardize radiology requisitions?
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Radiology stands as a central pillar of modern healthcare, non-invasively visualizing anatomy and physiology to guide critical diagnostic and treatment decisions. Over the last decade, the radiology community has made significant strides in standardizing its “outputs,” the radiology report [1]. Through initiatives like the various Reporting and Data Systems (RADS) for breast, liver, prostate, and thyroid imaging, we have improved communication, reduced ambiguity, and enhanced the clinical utility of our findings. However, a high-quality output depends fundamentally on a high-quality input.
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Contexto Educativo
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Springer
Licença CC
Sem licença CC