The need for uncertainty quantification in machine-assisted medical decision making

By Edmon Begoli, Tanmoy Bhattacharya, Dimitri Kusnezov. Nature Machine Intelligence, 2019

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a73eef4e0ec8e49be1c0e6abb04cc1967a681c50

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Citations in the corpus

Identifying Ethical Considerations for Machine Learning Healthcare Applications (2020, background): “…any ML-HCA built on such a classification would likely result in an even higher likelihood of de-escalation or withholding, thereby reducing the opportunity to improve outcomes for such conditions (Begoli et al. 2019; Fiske et al. 2019; Nabi 2018; Cohen et al. 2014; Ho 2019; Taljaard et al. 2014).”
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020, background): “The uncertainties that are obvious in these steps are the following: (i) selection and collection of training data, (ii) completeness and accuracy of training data, (iii) understanding the DL (or traditional machine learning) model with performance bounds and its limitations, and (iv) uncertainties ”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “However, these models have black-box nature and lack underlying theoretical foundation behind their decisions [8], therefore,”
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “Begoli et al. (2019) also discussed the need for UQ in machine learning-assisted medical decision-making.”
Single-Model Uncertainties for Deep Learning (2018, background): “[5], uncertainty quantification is a problem of paramount importance when deploying machine learning models in sensitive domains such as information security [72], engineering [82], transportation [87], and medicine [5], to name a few.”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, background): “To this end, Begoli and colleagues recommend development and adoption of systematic and pragmatic measures of uncertainty quantification in AI models (128).”
Multimodal data fusion for cancer biomarker discovery with deep learning (2023, background): “To be useful in clinical decision-making, models need to undergo extensive testing, be interpretable, and their predictions need to be accompanied by confidence or uncertainty measures 148,149 .”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “DL models are black-box which lacks fundamental underlying theory and these models essentially work by exploiting patterns and correlations without considering any causal link [116].”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “…above are prevalent not only in finance [20, 133] but also in justice (e.g., pretrial bail) [10], healthcare (e.g., prescription of life-altering medication) [24, 27, 72], and other settings (e.g., hiring) [43, 134, 160] broadly classified as consequential decision-making settings [20, 34, 45, 91].”
Trustworthy artificial intelligence (2020, background): “Another prominent stream of research concerned with the explainability of AI encompasses the quantification of uncertainties (Begoli et al. 2019).”