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Fragmented medical data hurts MLLM performance: this paper shows how a hierarchical medical knowledge graph can be used to engineer training data that substantially improves MLLM accuracy on complex clinical tasks.
MedXIAOHE leapfrogs closed-source systems on medical benchmarks by using entity-aware pretraining and RL-based reasoning, offering a new open-source foundation for medical AI.
