ATHENA-R1 is an RL-trained agent using 212 biomedical tools that achieves 94.7% accuracy on drug reasoning and 82.9% on treatment reasoning tasks, outperforming GPT-5 by 17.8 and 10.7 points respectively.
Real-World Evidence — What Is It and What Can It Tell Us? N Engl J Med
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Bidirectional feedback between physical and virtual systems is the defining property of digital twins, serving as an organizing principle for multi-scale hierarchies in biological and social organization.
A composite patient-trial generalizability score derived from trial eligibility criteria predicts fewer serious adverse events among real-world colorectal cancer patients who more closely match the trial population.
citing papers explorer
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An AI agent for treatment reasoning over a biomedical tool universe
ATHENA-R1 is an RL-trained agent using 212 biomedical tools that achieves 94.7% accuracy on drug reasoning and 82.9% on treatment reasoning tasks, outperforming GPT-5 by 17.8 and 10.7 points respectively.
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Digital Twins Need Feedback
Bidirectional feedback between physical and virtual systems is the defining property of digital twins, serving as an organizing principle for multi-scale hierarchies in biological and social organization.
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Assessing the Validity of a a priori Patient-Trial Generalizability Score using Real-world Data from a Large Clinical Data Research Network: A Colorectal Cancer Clinical Trial Case Study
A composite patient-trial generalizability score derived from trial eligibility criteria predicts fewer serious adverse events among real-world colorectal cancer patients who more closely match the trial population.