Abstract: An exemplary embodiment of the present invention relates to a system for identifying breast cancer based on at least one image of a patient's breast, the system comprising: an evaluation unit configured to analyze the image, wherein the evaluation unit generates a diagnosis result that labels the image as confident if the confidence in the evaluation result exceeds a given confidence level, and otherwise generates a diagnosis result that labels the image as unconfident, a request unit configured to generate an evaluation-request signal that requests an additional external evaluation of the image, if the diagnosis result labels the image as unconfident, and a transfer unit having an input port for receiving the result of the additional external evaluation, and an output port for outputting the diagnosis result of the evaluation unit, wherein the transfer unit is configured to block access to the diagnosis result of the evaluation unit until the result of the additional external evaluation has been received.

Abstract: A method for optimizing an optical aid by way of automatic measurement of the subjective visual performance, a method for producing a correspondingly optimized optical aid, an apparatus for producing optical aids, a computer program having a program code for carrying out the optimization method, which program can be run on a processor, and a non-transitory storage medium comprising the computer program stored thereon are disclosed. The method for optimizing the optical aid includes the automatic determination of subjective visual acuity using machine learning.

Abstract: A method for optimizing an optical aid by way of automatic measurement of the subjective visual performance, a method for producing a correspondingly optimized optical aid, an apparatus for producing optical aids, a computer program having a program code for carrying out the optimization method, which program can be run on a processor, and a non-transitory storage medium comprising the computer program stored thereon are disclosed. The method for optimizing the optical aid includes the automatic determination of subjective visual acuity using machine learning.

Abstract: An apparatus for ascertaining predicted subjective refraction data or predicted subjective correction values of an eye to be examined on the basis of objective refraction data of the eye to be examined is disclosed. The apparatus includes an evaluation device with a calculation unit, which ascertains the predicted subjective refraction data or predicted subjective correction values of the eye from the objective refraction data of the eye with a function. The function is a nonlinear multidimensional function or a family of nonlinear multidimensional functions, which is the result of training a regression model or classification model, wherein the regression model or classification model has been trained on the basis of a training data record, which, for a multiplicity of subjects, in each case includes at least objective refraction data and assigned ascertained subjective refraction data or assigned ascertained subjective correction values.