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LLMs show promise in polyp diagnosis, but deep learning framework leads in classification

By PulseAugur Editorial · [3 sources] ·

A new study evaluated the diagnostic accuracy of several large language models (LLMs) in classifying colorectal polyps using the PRIME dataset. Claude Opus 4 and Gemini 2.5 Pro demonstrated the highest accuracy in differentiating polyp subtypes, performing closest to expert consensus, though overall sensitivity and specificity did not meet clinical standards. Separately, a deep learning framework named PolypVision was developed for polyp classification and segmentation, achieving high performance on public datasets and offering a device-independent solution. AI

IMPACT Demonstrates LLMs' potential in medical image analysis, while also highlighting the need for further development and validation before clinical deployment.

RANK_REASON Two research papers presenting novel applications of AI in medical diagnosis and classification.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs show promise in polyp diagnosis, but deep learning framework leads in classification

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Ziyi Wang, Yuanmei Zhang, Baoying Ye, Yimei Jiang, Leilei Gu, Suncheng Xiang ·

    MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation

    arXiv:2511.01143v2 Announce Type: replace-cross Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry. However, current deep learning-based polyp segmentati…

  2. arXiv cs.AI TIER_1 English(EN) · Joshua C. Vences, William T. Tran, Nikko Gimpaya, Catharine M. Walsh, Rishad J. Khan, Robert Bechara, Asher C. Wiggins, Celine N. Rousan, Kaitlyn V. G. L. Morgado, Angie Ibrahim, Kevin H. M. Kuo, Daniel von Renteln, Alexander Hann, Dennis L. Shung, Micha… ·

    Performance of large language models in the optical diagnosis of colorectal polyps

    arXiv:2608.07543v1 Announce Type: cross Abstract: Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language models (MLLMs) showing potential for image-based diagnosis. We aimed to evaluate…

  3. arXiv cs.CV TIER_1 English(EN) · Hamidreza Bolhasani, Hamidreza Rastad, Amir Mohammad Akbari, Mohammad Tashakoripour, Parnian Asadollahi, Ata Khodami, Mojgan Forootan ·

    PolypVision: A Three-Stage Hierarchical Deep Learning Framework for Classification and Segmentation of Colorectal Polyps

    arXiv:2608.10649v1 Announce Type: new Abstract: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, predominantly arising from precancerous polyps. Accurate detection, segmentation, and endoscopic and histological classification of col…

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