Clinical Text Generation: Are We There Yet?
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View abstract on PubMed
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.
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.
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