Detecting defects in real-world industrial inspection often requires learning from only a few normal images per category. Recent few-shot anomaly detection methods have achieved strong performance by measuring feature differences between query images and normal references. However, visual differences do not always indicate genuine defects, as normal appearance variations can also produce high anomaly scores. Conversely, structural or logical anomalies may not produce strong local feature differences, making them difficult to detect. To address this limitation, we introduce VerdictAD, a training-free framework that refines existing anomaly detectors through visual decisions. Rather than replacing existing detectors, VerdictAD adopts a typed decision model to assess the query image and its local regions against a few normal references. By directly producing structured decision probabilities without text generation, the model provides complementary evidence for refining anomaly responses at different spatial scales. Building on this capability, we introduce two modules: Global Verdict Refinement (GVR) uses image-level decisions to refine the overall anomaly score, while Local Verdict Refinement (LVR) aggregates regional decisions to refine the anomaly map. Combined with a simple DINOv2-based detector, VerdictAD achieves competitive performance in one-shot and few-shot anomaly detection. More importantly, as a plug-and-play framework, it consistently improves existing anomaly detectors across MVTec AD, VisA, and MVTec LOCO AD, achieving strong results across one-shot and few-shot settings. Our code and experimental logs are publicly available at https://github.com/sgstq0811/VerdictAD-Jev-AD.
Current medical artificial intelligence (AI) systems are often task-specific, poorly integrated, and difficult to interpret, limiting support for multimodal clinical workflows. We developed and evaluated EyeAgent for ophthalmic decision support. EyeAgent comprises three layers including 54 task-specific EyeTools, 32 clinical skills, and a modular large language model reasoning core with interchangeable backbones. A multi-level evaluation distinguished this scope from empirical system-level validation. Component-level evaluation assessed newly developed models on held-out splits of datasets totaling 871,862 samples. System-level evaluation included illustrative cases of workflow breadth, a 200-case expert-rating study of output quality, and an exploratory multicenter reader study comparing unassisted and EyeAgent-assisted performance among 27 ophthalmologists from 11 centers. Skill-level and backbone ablations further assessed orchestration and robustness. In illustrative cases, EyeAgent showed task-specific advantages over GPT-5.5 in lesion quantitation, cross-modality synthesis, and uncertainty-aware reporting. In the expert-rating study, EyeAgent achieved an F1 score of 0.84 for skill selection, with 82.7%-92.0% of outputs rated acceptable or better across six clinical dimensions. In the exploratory multicenter reader study, diagnostic accuracy increased from 72.8% unassisted to 91.9% assisted (difference, 19.1 percentage points (pp); 95% CI, 13.6-24.7; Holm-adjusted P<0.001). Report completeness increased from 55.8% to 75.6% (difference, 19.8 pp; 95% CI, 14.8-24.7; Holm-adjusted P<0.001). Across the evaluated settings, EyeAgent supported interpretable, evidence-guided reasoning and showed potential for human-AI collaboration. Broader real-world effectiveness and generalizability require prospective and cross-population validation.