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A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

Author

Listed:
  • Dani Kiyasseh

    (Cedars-Sinai Medical Center)

  • Aaron Cohen

    (Flatiron Health
    New York University School of Medicine)

  • Chengsheng Jiang

    (Flatiron Health)

  • Nicholas Altieri

    (Flatiron Health)

Abstract

A clinical artificial intelligence (AI) system is often validated on data withheld during its development. This provides an estimate of its performance upon future deployment on data in the wild; those currently unseen but are expected to be encountered in a clinical setting. However, estimating performance on data in the wild is complicated by distribution shift between data in the wild and withheld data and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for evaluating AI systems on data in the wild. Through experiments on AI systems developed for dermatology images, histopathology patches, and clinical notes, we show that SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations. These capabilities can contribute to the deployment of trustworthy and ethical AI systems in medicine.

Suggested Citation

  • Dani Kiyasseh & Aaron Cohen & Chengsheng Jiang & Nicholas Altieri, 2024. "A framework for evaluating clinical artificial intelligence systems without ground-truth annotations," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46000-9
    DOI: 10.1038/s41467-024-46000-9
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