The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process
Open accessAbstract
Different terms such astrust,certainty, anduncertaintyare of great importance in the real world and play a critical role in artificial intelligence (AI) applications. The implied assumption is that the level of trust in AI can be measured in different ways. This principle can be achieved by distinguishing uncertainties in predicting AI methods used in medical studies. Hence, it is necessary to propose effective uncertainty quantification (UQ) and measurement methods to have trustworthy AI (TAI) clinical decision support systems (CDSSs). In this study, we present practical guidelines for developing and using UQ methods while applying various AI techniques for medical data analysis.
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AI and ML algorithms can analyze large amounts of data and make informed decisions based on that data, allowing robots to make better decisions and take appropriate actions.
“Improved Decision Making: AI and ML algorithms can analyze large amounts of data and make informed decisions based on that data, allowing robots to make better decisions and take appropriate actions [51] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [51]
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
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- Authors
- Abdar, M., Khosravi, A., Islam, S. M. S., Acharya, U. R., & Vasilakos, A. V.
- Affiliations
- Deakin University (AU)
- Asia University (TW)
- Fuzhou University (CN)
- ORCID
- Venue
- IEEE Systems, Man, and Cybernetics Magazine
- Published
- 2022-07
- Volume
- 8
- Issue
- 3
- Pages
- 28-40
- ISSN
- 2333-942X (ISSN-L)
- 2380-1298
- OpenAlex
- W4285809023
- Cited by
- 55
- References
- 17
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 8 · 2023: 15 · 2022: 5
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- IEEE Systems Man and Cybernetics Magazine (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Australian Research Council (DP190102181)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- uncertainty quantification, trustworthy AI, clinical decision support systems, medical data analysis, trust in AI, artificial intelligence in healthcare, uncertainty measurement, AI decision-making
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