Benchmarking large language model-based agent systems for clinical decision tasks - Takeaways - MDSpire
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Evaluating the Performance of AI Agent Systems in Clinical Decision-Making Tasks

  • By

  • Yunsong Liu

  • Zunamys I. Carrero

  • Xiaofeng Jiang

  • Dyke Ferber

  • Georg Wölflein

  • Li Zhang

  • Sanddhya Jayabalan

  • Tim Lenz

  • Zhouguang Hui

  • Jakob Nikolas Kather

  • February 18, 2026

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  • 1

    Agentic AI systems show potential in healthcare but lack systematic benchmarking of real-world performance.

  • 2

    The study evaluated OpenManus and Manus across three benchmark families, revealing modest accuracy gains.

  • 3

    Agent systems achieved 60.3% accuracy in AgentClinic MedQA but only 8.6% on the Humanity’s Last Exam.

  • 4

    Resource demands increased significantly, with over 10× token usage and more than 2× latency compared to baseline models.

  • 5

    Current agentic designs provide limited performance benefits at high computational costs, highlighting the need for improvement.

Original Source(s)

Benchmarking large language model-based agent systems for clinical decision tasks

  • by Yunsong Liu, Zunamys I. Carrero, Xiaofeng Jiang, Dyke Ferber, Georg Wölflein, Li Zhang, Sanddhya Jayabalan, Tim Lenz, Zhouguang Hui, Jakob Nikolas Kather

  • February 18, 2026

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