A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination

2025-04-16

Journal of Biomedical Informatics

AI Summary

This study systematically reviews NLP applications (2020–2024) for detecting, correcting, and mitigating medical misinformation—including clinical errors, false information, and LLM hallucinations. Following the PRISMA-ScR guidelines, it synthesizes BERT-based models, large language models (LLMs), rule-based systems, and hybrid approaches across tasks such as text classification, generative correction, and credibility scoring. Its primary contribution is a novel unified conceptual framework that integrates NLP strategies for all three problem types, alongside a cross-task methodology emphasizing clinical context modeling and explainable evaluation. The review identifies six core task-specific performance outcomes and critical bottlenecks: data privacy constraints, insufficient dynamic contextual modeling, and absence of standardized clinical validation metrics. Collectively, these findings provide both theoretical foundations and a practical roadmap for developing safe, transparent, and clinically deployable medical NLP systems.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Misinformation & Fake News

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Mitigating misinformation and disinformation

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-ScR 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.

Problem

• Detect and correct medical errors using NLP
• Address healthcare misinformation with NLP techniques
• Mitigate medical hallucinations through NLP applications

Innovation

• NLP detects medical errors and misinformation
• NLP corrects medical inaccuracies and hallucinations
• NLP improves healthcare data reliability