🩺 Open-source large language models now perform well across various clinical natural language processing tasks, even though they never see electronic health records. Where do they pick up that clinical knowledge? 🚀 We are excited to share our CHIL 2025 paper “Diagnosing our Datasets” with @MonicaNAgrawal and @david_sontag. Beyond releasing the MedLingo dataset for clinical jargon understanding, we probe the pre-training datasets behind popular models to uncover new insights on how online medical sources drive LLMs’ performance. 📄 Paper: arxiv.org/abs/2505.15024 #aiforgood #aiforhealth #healthcareinnovation #machinelearningsolutions #CHIL2025

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