医学影像领域的小伙伴可以看看这篇，CARE-X 把胸片分类、定位、报告生成整合到一个模型里，还靠工具调用做解剖测量，临床实用性比纯生成模型强不少。
CARE-X 在生成骨干网络上增加焦损失分类与复合损失定位头，与语言建模目标联合训练，使胸片报告生成、发现分类和空间定位互相增强。配合任务级奖励优化的 DAPO 策略，该方法在四个报告生成基准的大部分指标上达到最优，ReXVQA 视觉问答准确率 94.0%，比次优基线高 6.0 个百分点。此外，将 Qwen3-VL-4B-Instruct 与可调用确定性测量工具的原生工具能力结合，在五个依赖测量的诊断场景中平均 F1 比仅靠感知的基线高 43.6 个百分点。
CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend. Today's Vision-Language Models (VLMs) treat these as separate problems, if they address them at all, leaving a gap between what radiologists need and what generative models provide. We introduce CARE-X, a chest X-ray VLM that narrows this gap by unifying auxiliary discriminative supervision with reward-aligned generation. CARE-X augments its generative backbone with focal-loss classification and composite-loss grounding heads, co-trained alongside the language-modeling objective. This auxiliary supervision produces discriminative diagnostic predictions with tunable decision thresholds and precise spatial localization while also improving report quality, providing evidence that structured prediction and generation reinforce one another. Building on this foundation, Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) leverages task-specific reward signals for report generation, visual question answering (VQA), and spatial grounding, directly optimizing the clinical quality metrics that matter in practice. The result is state-of-the-art performance on the majority of metrics across four report-generation benchmarks, 94.0% VQA accuracy on ReXVQA (+6.0 pp over the next-best baseline), and generative spatial decoding that reaches near parity with dedicated detection heads. Separately, to address measurement-dependent diagnoses, we couple Qwen3-VL-4B-Instruct with native tool-calling capabilities for invoking deterministic measurement tools, while retaining full visual access to the image. This hybrid inference yields +43.6 pp average F1 over perception-only baselines across five measurement-dependent conditions.