Abstract: Introduced here are diagnostic platforms able to verify subjects included in images in a time- and resource-efficient manner. By assessing the degree of consistency between multiple characterizations of an attribute that are derived from different data, a diagnostic platform can determine the likelihood that an image includes a given subject. An attribute could be characterized based on metadata that accompanies an image captured during a diagnostic session, information provided by an individual as part of the diagnostic session, an output produced by a classification model upon being applied to the image, or any combination thereof.

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 verify subjects included in images in a time- and resource-efficient manner. By assessing the degree of consistency between multiple characterizations of an attribute that are derived from different data, a diagnostic platform can determine the likelihood that an image includes a given subject. An attribute could be characterized based on metadata that accompanies an image captured during a diagnostic session, information provided by an individual as part of the diagnostic session, an output produced by a classification model upon being applied to the image, or any combination thereof.

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.