Patient-facing medical LLMs are typically evaluated for clinical knowledge and safety, but clinically correct answers can still fail patients by missing fear, mishandling uncertainty, or offering emotionally unsafe reassurance. We introduce MedEQ, a benchmark for clinical emotional intelligence in medical communication. MedEQ includes 413 clinically grounded consultation scenarios across 51 specialties and 7,336 expert-refined rubric items over 21 core dimensions. These expert-validated rubrics operationalize emotional cue recognition, empathic adequacy, ethical emotional regulation, and affective safety, enabling diagnostic evaluation rather than a single holistic empathy score. Using an LLM-as-judge rubric pipeline, we find that strong contemporary models often handle surface compassion, safety, and response structure, yet remain weak on deeper emotional inference, personalization, mixed emotions, cultural context, and real-time responses to illness-related distress. We further conduct a proof-of-concept rubric-guided reinforcement learning study, showing that MedEQ-derived rubric signals can transfer to an independent emotional intelligence benchmark: rubric-guided training of Qwen3-32B improves EQ-Bench performance from 42.42 to 81.49 while preserving or slightly improving standard medical benchmark scores. MedEQ therefore makes clinical emotional intelligence measurable and decomposable, and provides evidence that expert-refined rubrics can support the evaluation and improvement of patient-facing medical AI.

Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relationship between dataset size and the test loss of an LLM. However, the marginal gains from simply increasing data for CPT diminish rapidly, yielding suboptimal data utilization and inefficient training. To address this challenge, we propose a novel perplexity-aware data scaling law to establish a predictive relationship between the perplexity landscape of domain-specific data and the test loss. Our approach leverages the pre-trained model’s own perplexity on domain data as a proxy for estimating the knowledge gap, effectively quantifying the informational perplexity landscape of candidate training samples. By fitting this scaling law across diverse perplexity regimes, we enable adaptive selection of high-utility data subsets, prioritizing content that maximizes knowledge absorption while minimizing redundancy and noise. Extensive experiments on both medical and general-domain benchmarks demonstrate that our method consistently identifies near-optimal training subsets, achieving superior performance with significantly reduced data consumption.