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Why do probabilistic clinical models fail to transport between sites.

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Artificial intelligence (AI) in healthcare often fails to perform well in new clinical settings. This study explains why AI models fail to transport and suggests solutions to improve their reliability across different sites.

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Area of Science:

  • Healthcare AI
  • Clinical Informatics
  • Machine Learning in Medicine

Background:

  • Artificial intelligence (AI) models demonstrate high performance in healthcare settings.
  • A significant challenge arises when these models are deployed in new clinical environments, leading to decreased performance.
  • This phenomenon, known as failure to transport, is a critical issue in clinical AI adoption.

Purpose of the Study:

  • To explain the reasons behind the failure to transport of AI models in healthcare.
  • To identify sources of this failure, categorizing them into experimenter-controlled and data-inherent factors.
  • To propose a method for mitigating the impact of site-specific clinical practices on AI model performance.

Main Methods:

  • The study presents a perspective on the failure to transport phenomenon.
  • It analyzes common sources of failure, differentiating between experimental and inherent clinical data-generating processes.
  • A potential solution is proposed to isolate the influence of clinical practices on data.

Main Results:

  • AI models trained in one clinical setting often perform poorly in others.
  • Failure to transport is a common and expected outcome.
  • Site-specific clinical practices significantly alter data distributions, impacting model generalizability.

Conclusions:

  • The failure to transport of AI models in healthcare is a predictable issue.
  • Understanding and addressing site-specific data variations is crucial for reliable AI deployment.
  • Proposed solutions aim to disentangle clinical practice effects from disease patterns for more robust AI models.