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Large language models encode clinical knowledge

Author

Listed:
  • Karan Singhal

    (Google Research)

  • Shekoofeh Azizi

    (Google Research)

  • Tao Tu

    (Google Research)

  • S. Sara Mahdavi

    (Google Research)

  • Jason Wei

    (Google Research)

  • Hyung Won Chung

    (Google Research)

  • Nathan Scales

    (Google Research)

  • Ajay Tanwani

    (Google Research)

  • Heather Cole-Lewis

    (Google Research)

  • Stephen Pfohl

    (Google Research)

  • Perry Payne

    (Google Research)

  • Martin Seneviratne

    (Google Research)

  • Paul Gamble

    (Google Research)

  • Chris Kelly

    (Google Research)

  • Abubakr Babiker

    (Google Research)

  • Nathanael Schärli

    (Google Research)

  • Aakanksha Chowdhery

    (Google Research)

  • Philip Mansfield

    (Google Research)

  • Dina Demner-Fushman

    (National Library of Medicine)

  • Blaise Agüera y Arcas

    (Google Research)

  • Dale Webster

    (Google Research)

  • Greg S. Corrado

    (Google Research)

  • Yossi Matias

    (Google Research)

  • Katherine Chou

    (Google Research)

  • Juraj Gottweis

    (Google Research)

  • Nenad Tomasev

    (DeepMind)

  • Yun Liu

    (Google Research)

  • Alvin Rajkomar

    (Google Research)

  • Joelle Barral

    (Google Research)

  • Christopher Semturs

    (Google Research)

  • Alan Karthikesalingam

    (Google Research)

  • Vivek Natarajan

    (Google Research)

Abstract

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA3, MedMCQA4, PubMedQA5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics6), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today’s models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications.

Suggested Citation

  • Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Large language models encode clinical knowledge," Nature, Nature, vol. 620(7972), pages 172-180, August.
  • Handle: RePEc:nat:nature:v:620:y:2023:i:7972:d:10.1038_s41586-023-06291-2
    DOI: 10.1038/s41586-023-06291-2
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    Citations

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    Cited by:

    1. Ching-Nam Hang & Pei-Duo Yu & Roberto Morabito & Chee-Wei Tan, 2024. "Large Language Models Meet Next-Generation Networking Technologies: A Review," Future Internet, MDPI, vol. 16(10), pages 1-29, October.
    2. Chen Gao & Xiaochong Lan & Nian Li & Yuan Yuan & Jingtao Ding & Zhilun Zhou & Fengli Xu & Yong Li, 2024. "Large language models empowered agent-based modeling and simulation: a survey and perspectives," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-24, December.
    3. Zhenjia Chen & Zhenyuan Lin & Ji Yang & Cong Chen & Di Liu & Liuting Shan & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Soroosh Tayebi Arasteh & Tianyu Han & Mahshad Lotfinia & Christiane Kuhl & Jakob Nikolas Kather & Daniel Truhn & Sven Nebelung, 2024. "Large language models streamline automated machine learning for clinical studies," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Juexiao Zhou & Xiaonan He & Liyuan Sun & Jiannan Xu & Xiuying Chen & Yuetan Chu & Longxi Zhou & Xingyu Liao & Bin Zhang & Shawn Afvari & Xin Gao, 2024. "Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Yujin Oh & Sangjoon Park & Hwa Kyung Byun & Yeona Cho & Ik Jae Lee & Jin Sung Kim & Jong Chul Ye, 2024. "LLM-driven multimodal target volume contouring in radiation oncology," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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