Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jun 7, 2023
Open Peer Review Period: Jun 7, 2023 - Jun 22, 2023
Date Accepted: Oct 21, 2024
(closed for review but you can still tweet)
Task-Specific Transformer-Based Language Models in Health Care: A Scoping Review
ABSTRACT
Background:
In the field of artificial intelligence, language models, which are used to convey knowledge in the medical domain, have rapidly increased in number. However, no comprehensive review is available to guide researchers in constructing and applying language models for medical applications.
Objective:
We aim to leverage the power of these language models to improve healthcare by addressing the challenges in the six tasks we reviewed.
Methods:
We present potential solutions to the identified limitations to provide useful insights for future research in natural language processing and the development of language models for medical applications.
Results:
We surveyed studies on medical transformer-based language models, categorizing them into six tasks: dialogue generation, question-answering, summarization, text classification, sentiment analysis, and named entity recognition.
Conclusions:
By proposing potential solutions, we hope to facilitate the creation of more effective and accurate language models that can be utilized to enhance healthcare delivery and improve patient outcomes.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.