Uncertainty Quantification for Machine Learning in Healthcare: A Survey

Leopoldo Julian Lechuga Lopez, Shaza Elsharief, Dhiyaa Al Jorf, Firas Darwish, Congbo Ma, Farah E. Shamout

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.