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Clinical Text Generation: Are We There Yet?

Nicolas Hiebel1, Olivier Ferret2, Karën Fort3

  • 1Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France; email: nicolas.hiebel@universite-paris-saclay.fr, aurelie.neveol@cnrs.fr.

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Summary
This summary is machine-generated.

Generative artificial intelligence (AI), or large language models, can create biomedical text. This review covers text generation methods, evaluation, and ethical considerations for clinical applications.

Keywords:
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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Generative artificial intelligence (AI), specifically large language models (LLMs), are increasingly adopted in the biomedical domain.
  • LLMs are utilized for various text processing tasks like classification, information extraction, and decision support.

Purpose of the Study:

  • To review past and current methods for unstructured text generation using LLMs.
  • To discuss methods for evaluating open text generation where reference texts are unavailable.
  • To explore clinical applications and ethical considerations of AI-driven text generation.

Main Methods:

  • Review of existing literature on generative AI text production methods.
  • Analysis of techniques for evaluating open-ended text generation.
  • Discussion of ethical implications and potential risks associated with AI-generated biomedical text.

Main Results:

  • Generative AI offers advanced capabilities for producing unstructured text in the biomedical field.
  • Evaluation of open text generation presents unique challenges compared to reference-based methods.
  • Clinical applications include clinical note generation and synthetic data creation for health data secondary use.

Conclusions:

  • High-quality, ethically designed text generation using AI holds significant promise for biomedical applications.
  • Awareness of risks such as overconfidence and bias in AI outputs is crucial for responsible implementation.
  • Further research is needed to ensure the safe and effective deployment of generative AI in healthcare.