Uncertainty Quantification for Machine Learning in Healthcare: A Survey
Journal:
arXiv
Published Date:
May 4, 2025
Abstract
Uncertainty Quantification (UQ) is pivotal in enhancing the robustness,
reliability, and interpretability of Machine Learning (ML) systems for
healthcare, optimizing resources and improving patient care. Despite the
emergence of ML-based clinical decision support tools, the lack of principled
quantification of uncertainty in ML models remains a major challenge. Current
reviews have a narrow focus on analyzing the state-of-the-art UQ in specific
healthcare domains without systematically evaluating method efficacy across
different stages of model development, and despite a growing body of research,
its implementation in healthcare applications remains limited. Therefore, in
this survey, we provide a comprehensive analysis of current UQ in healthcare,
offering an informed framework that highlights how different methods can be
integrated into each stage of the ML pipeline including data processing,
training and evaluation. We also highlight the most popular methods used in
healthcare and novel approaches from other domains that hold potential for
future adoption in the medical context. We expect this study will provide a
clear overview of the challenges and opportunities of implementing UQ in the ML
pipeline for healthcare, guiding researchers and practitioners in selecting
suitable techniques to enhance the reliability, safety and trust from patients
and clinicians on ML-driven healthcare solutions.
