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- W4385264908 abstract "Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications." @default.
- W4385264908 created "2023-07-27" @default.
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- W4385264908 date "2023-07-26" @default.
- W4385264908 modified "2023-10-05" @default.
- W4385264908 title "Interpretable artificial intelligence in radiology and radiation oncology" @default.
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- W4385264908 doi "https://doi.org/10.1259/bjr.20230142" @default.
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