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Large multimodal models show mixed results for medical image PHI detection

By PulseAugur Editorial · [1 sources] ·

Researchers evaluated large multimodal models (LMMs) like GPT-4o and Gemini 2.5 Flash for detecting protected health information (PHI) in medical images. While LMMs showed improved text recognition (lower Word Error Rate) compared to traditional OCR methods, this did not always translate to higher overall PHI detection accuracy. The study found that LMMs were most effective on complex imprint patterns and offered recommendations for selecting and deploying these models in healthcare settings. AI

IMPACT LMMs show potential for improving PHI detection in medical images, particularly for complex cases, guiding future healthcare AI deployments.

RANK_REASON The cluster contains an academic paper detailing research findings on the application of large multimodal models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Large multimodal models show mixed results for medical image PHI detection

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The cluster contains an academic paper detailing research findings on the application of large multimodal models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tuan Truong, Guillermo Jimenez Perez, Pedro Osorio, Matthias Lenga ·

    Towards Selection of Large Multimodal Models as Engines for Burned-in Protected Health Information Detection in Medical Images

    arXiv:2511.02014v2 Announce Type: replace Abstract: The detection of Protected Health Information (PHI) in medical imaging is critical for safeguarding patient privacy and ensuring compliance with regulatory frameworks. Traditional detection methodologies predominantly utilize Op…

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