Use of Explainable AI Algorithm Revealing Longitudinal Changes in Practice Patterns and Toxicity Models

S. Su, C. Mayo, B.S. Rosen, E. Covington, Z. Zhang,A.K. Bryant,S.G. Allen,K.A. Morales Rivera, D.M. Edwards, J. Takayesu, D.J. Herr, S.R. Miller, S.N. Regan, M.P. Dykstra, G.Y. Sun, A.L. Elaimy, M.L. Mierzwa

International journal of radiation oncology, biology, physics（2023）

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摘要

As OAR doses were systematically reduced, statistical and AI models evidence highlighted contralateral SMG dose as important to both dysphagia and xerostomia for clinical practice change. The "real-world" database + eAI + visualization dashboards provided a method for continuous learning as clinical practice changes.

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explainable explainable algorithm,toxicity,practice patterns,longitudinal changes,models

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