A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination
Journal:
arXiv
Published Date:
Apr 16, 2025
Abstract
Objective: This review aims to explore the potential and challenges of using
Natural Language Processing (NLP) to detect, correct, and mitigate medically
inaccurate information, including errors, misinformation, and hallucination. By
unifying these concepts, the review emphasizes their shared methodological
foundations and their distinct implications for healthcare. Our goal is to
advance patient safety, improve public health communication, and support the
development of more reliable and transparent NLP applications in healthcare.
Methods: A scoping review was conducted following PRISMA guidelines,
analyzing studies from 2020 to 2024 across five databases. Studies were
selected based on their use of NLP to address medically inaccurate information
and were categorized by topic, tasks, document types, datasets, models, and
evaluation metrics.
Results: NLP has shown potential in addressing medically inaccurate
information on the following tasks: (1) error detection (2) error correction
(3) misinformation detection (4) misinformation correction (5) hallucination
detection (6) hallucination mitigation. However, challenges remain with data
privacy, context dependency, and evaluation standards.
Conclusion: This review highlights the advancements in applying NLP to tackle
medically inaccurate information while underscoring the need to address
persistent challenges. Future efforts should focus on developing real-world
datasets, refining contextual methods, and improving hallucination management
to ensure reliable and transparent healthcare applications.
