mlsanityData to 14 Oct 2026

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

arXiv 2505.00008 ↗First posted 16 Apr 2025cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 59 of its 154 references that are in the map. First posted on arXiv 16 Apr 2025.

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59 of 154 references.

References · 59

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Reviewcited by 1
  2. 2026An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verificationcited by 0