Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Article
Article Title | Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023) |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Seoni, Silvia, Jahmunah, Vicnesh, Salvi, Massimo, Barua, Prabal Datta, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 165 |
Article Number | 107441 |
Number of Pages | 28 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2023.107441 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S001048252300906X |
Abstract | Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations. |
Keywords | Bayesian models; Uncertainty techniques ; Machine learning models ; Deep learning models ; PRISMA; Images; Signals; Healthcare |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Polytechnic University of Turin, Italy |
Nanyang Polytechnic, Singapore | |
School of Business | |
University of Technology Sydney | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z26w7/application-of-uncertainty-quantification-to-artificial-intelligence-in-healthcare-a-review-of-last-decade-2013-2023
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