Clinical Text Generation: Are We There Yet?
Abstract
Generative artificial intelligence (AI), 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, and decision support. In this article, 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 of the risks involved with generative AI such as overconfidence in outputs due to anthropomorphism and the risk of representational and allocation harms due to biases.
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