Abstract: Machine learning models used in medical diagnosis should be validated after being deployed in order to reduce the number of misdiagnoses. Validation processes presented here assess a performance of the machine learning model post-deployment. In post-deployment validation, the validation process monitoring can include: (1) monitoring to ensure a model performs as well as a reference member such as another machine learning model, and (2) monitoring to detect anomalies in data. This post-deployment validation helps identify low-performing models that are already deployed, so that relevant parties can quickly take action to improve either the machine learning model or the input data.

Abstract: Introduced here are diagnostic platforms able to optimize computer-aided diagnostic (CADx) models by simulating the optical performance of an imaging device based on its mechanical components. For example, a diagnostic platform may acquire a source image associated with a confirmed diagnosis of a medical condition, simulate optical performance based on design data corresponding to a virtual prototype of the imaging device, generate a training image by altering the source image based on the optical performance, apply a diagnostic model to the training image, and then determine whether the performance of the diagnostic model meets a specified performance threshold. If the diagnostic model fails to meet the specified performance threshold, the diagnostic platform can automatically optimize the diagnostic model for the imaging device by altering its underlying algorithm(s).

Abstract: Machine learning models used in medical diagnosis should be validated before being deployed in order to reduce the number of misdiagnoses. Validation processes presented here assess a performance of the machine learning model pre-deployment. In one or more examples, prior to the deployment of the machine learning model, the validation process assesses (1) whether a model achieves high enough performance to be deployed, and (2) that the process by which the performance metrics were computed was both sanitary and comprehensive. This pre-deployment validation helps prevent low-performing models from being deployed.

Abstract: Machine learning models used in medical diagnosis should be validated after being deployed in order to reduce the number of misdiagnoses. Validation processes presented here assess a performance of the machine learning model post-deployment. In post-deployment validation, the validation process monitoring can include: (1) monitoring to ensure a model performs as well as a reference member such as another machine learning model, and (2) monitoring to detect anomalies in data. This post-deployment validation helps identify low-performing models that are already deployed, so that relevant parties can quickly take action to improve either the machine learning model or the input data.