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Children's National Health System

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Representative Papers

A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI

Mar 28, 2026

Current AI models exhibit insufficient performance in surgical image analysis tasks such as neurosurgical instrument detection, falling short of clinical requirements. This study systematically evaluates the performance of state-of-the-art billion-parameter vision-language models—representative of 2026-level advancements—in surgical instrument detection scenarios and conducts scaling experiments varying model size and training duration. The findings reveal that merely increasing model scale or computational resources yields limited performance gains; instead, non-scalable factors such as annotation quality and domain-specific adaptation play a decisive role. Even with cutting-edge architectures and extensive training resources, improvements in instrument detection accuracy remain marginal, and consistent performance bottlenecks persist across diverse model architectures.

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Latest Papers

A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI

Mar 28, 2026

Current AI models exhibit insufficient performance in surgical image analysis tasks such as neurosurgical instrument detection, falling short of clinical requirements. This study systematically evaluates the performance of state-of-the-art billion-parameter vision-language models—representative of 2026-level advancements—in surgical instrument detection scenarios and conducts scaling experiments varying model size and training duration. The findings reveal that merely increasing model scale or computational resources yields limited performance gains; instead, non-scalable factors such as annotation quality and domain-specific adaptation play a decisive role. Even with cutting-edge architectures and extensive training resources, improvements in instrument detection accuracy remain marginal, and consistent performance bottlenecks persist across diverse model architectures.

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