期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 221, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2022.106927
关键词
Precision medicine; Decision making; Artificial intelligence; Computer model calibration; Gaussian process modeling
类别
资金
- NIH [R01-CA233487]
This paper presents a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. It combines Gaussian process models with deep neural networks to quantify the uncertainty of treatment outcomes given by physicians and AI recommendations, providing guidance for clinical physicians and improving AI models performance.
In the precision medicine era, there is a growing need for precision radiotherapy where the planned radiation dose needs to be optimally determined by considering a myriad of patient-specific information in order to ensure treatment efficacy. Existing artificial-intelligence (AI) methods can recommend radiation dose prescriptions within the scope of this available information. However, treating physicians may not fully entrust the AI's recommended prescriptions due to known limitations or at instances when the AI recommendation may go beyond physicians' current knowledge. This paper lays out a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. Towards this goal, Gaussian process (GP) models are integrated with deep neural networks (DNNs) to quantify the uncertainty of the treatment outcomes given by physicians and AI recommendations, respectively, which are further used as a guideline to educate clinical physicians and improve AI models performance. The proposed method is demonstrated in a comprehensive dataset where patient-specific information and treatment outcomes are prospectively collected during radiotherapy of 67 non-small cell lung cancer (NSCLC) patients and are retrospectively analyzed.(c) 2022 Elsevier B.V. All rights reserved.
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