Clinical Text Generation: Are We There Yet?

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.