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August 20, 2025Proceedings of the National Academy of SciencesOpen Access

Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine

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Authors

SCSamuel D. CurtisJohns Hopkins UniversitySPSambit PandaJohns Hopkins UniversityALAdam LiJohns Hopkins University

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Overview

MIGHT method effectively quantifies uncertainty in AI predictions for enhancing biomedical error control, suggesting new insights.

Key Points

  • MIGHT method accurately quantifies uncertainty, improving trust in AI predictions in biomedical settings.
  • Performance estimates on ccfDNA data showed lower coefficients of variation than traditional algorithms like support vector machines.
  • The method integrates cross-validation and calibration within a nonparametric ensemble approach for enhanced reliability.
  • This research highlights the need for uncertainty quantification in AI to ensure accurate real-world data interpretation.

Cite This Study

Curtis et al. (2025) studied this question.

synapsesocial.com/papers/68af4cd3ad7bf08b1ead5fa3https://doi.org/10.1073/pnas.2424203122
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