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MLLMs approach radiologist performance in malignancy prediction from mammograms

By PulseAugur Editorial · [1 sources] ·

A recent study benchmarked four multimodal large language models (MLLMs) against radiologists in interpreting mammograms for breast density, BI-RADS assessment, biopsy candidacy, and malignancy prediction. While radiologists generally outperformed MLLMs in categorical tasks, specific masked MLLMs, particularly Muse Spark and Claude Sonnet 4.6, approached human performance in estimating continuous malignancy probabilities. The findings suggest a potential adjunctive role for MLLMs in mammography analysis, especially when provided with lesion masks. AI

IMPACT Selected MLLMs show potential as adjunctive tools for radiologists in mammography, particularly for malignancy probability estimation.

RANK_REASON Academic paper presenting benchmark results of AI models against human experts. [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 →

MLLMs approach radiologist performance in malignancy prediction from mammograms

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Academic paper presenting benchmark results of AI models against human experts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ali Abbasian Ardakani, Afshin Mohammadi, Taha Yusuf Kuzan, Beyza Nur Kuzan, Alisa Mohebbi, Masume Behruzi, Hamid Khorshidi, Ashkan Ghorbani, Elham Asadiara, Zeinab Khorshidi Lotfi, Ansar Rahman, Nedim Christoph Beste, U. Rajendra Acharya, Sepideh Hatamik… ·

    From Density to Biopsy Decisions and Malignancy Prediction: A Benchmark Study of Multimodal Large Language Models Against Radiologists in Digital and Contrast-Enhanced Mammography

    arXiv:2609.14676v1 Announce Type: new Abstract: Purpose: To compare four multimodal large language models (MLLMs) with radiologists of varying expertise in breast density assessment, BI-RADS assessment, biopsy candidacy determination, and continuous malignancy probability estimat…

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