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Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored.
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Jamil Zaghir
Marco Naguib
Mina Bjelogrlic
Journal of Medical Internet Research
Laboratoire d'Informatique de Grenoble
Biologie Intégrée du Globule Rouge
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé
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Zaghir et al. (Tue,) studied this question.
www.synapsesocial.com/papers/68e58de3b6db643587529742 — DOI: https://doi.org/10.2196/60501
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