Article Dans Une Revue Annual Review of Biomedical Data Science Année : 2025

Clinical text generation: Are we there yet?

Résumé

Generative Artificial intelligence, operationalized as Large language Models, is increasingly used in the biomedical field to assist with a range of text processing tasks including text classification, information extraction or decision support. In this paper, we focus on the primary purpose of generative language models, namely the production of unstructured text. We review past and current methods used to generate text as well as methods for evaluating open text generation, i.e. in contexts where no reference text is available for comparison. We discuss clinical applications that can benefit from high quality, ethically designed, text generation, such as clinical note generation and synthetic text generation in support of secondary use of health data. We also raise awareness to risks involved with generative AI such as overconfidence in outputs due to anthropomorphism and risk of representational and allocation harms due to biases.

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hal-05055957 , version 1 (05-05-2025)

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Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol. Clinical text generation: Are we there yet?. Annual Review of Biomedical Data Science, 2025, 8, pp.173-198. ⟨10.1146/annurev-biodatasci-103123-095202⟩. ⟨hal-05055957⟩
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