EHRBench uses an EHR-LLM-KB pipeline to automatically create 960,067 reliable QA items spanning diagnosis, treatment, and prognosis for large-scale LLM evaluation in clinical decision making.
arXiv preprint arXiv:2406.06331 , year=
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Introduces BanglaMedVQA dataset of clinically validated image-question-answer pairs and benchmarks foundation models, finding substantially lower performance than on English MedVQA especially on diagnostic questions.
AROMA combines text, graph topology, and protein sequences with augmented reasoning and two-stage optimization to deliver more accurate and interpretable predictions of genetic perturbation effects in virtual cells, outperforming baselines even in zero-shot and long-tail settings.
citing papers explorer
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EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
EHRBench uses an EHR-LLM-KB pipeline to automatically create 960,067 reliable QA items spanning diagnosis, treatment, and prognosis for large-scale LLM evaluation in clinical decision making.
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How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking
Introduces BanglaMedVQA dataset of clinically validated image-question-answer pairs and benchmarks foundation models, finding substantially lower performance than on English MedVQA especially on diagnostic questions.
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AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling
AROMA combines text, graph topology, and protein sequences with augmented reasoning and two-stage optimization to deliver more accurate and interpretable predictions of genetic perturbation effects in virtual cells, outperforming baselines even in zero-shot and long-tail settings.