A computer-implemented method for optimizing a clinical workflow, the clinical workflow resulting in acquisition of one or more medical images (204, 310) during a patient exam, the method comprising the steps of: - obtaining workflow metadata (022, 202, 212) indicative of the clinical workflow, wherein the obtaining the workflow metadata comprises obtaining a system log of a medical apparatus or medical system used in carrying out the clinical workflow; - obtaining a viewer log of an image viewer used by a physician to review the one or more medical images, the viewer log being indicative of one or more viewing actions performed by the physician using the image viewer; - estimating a diagnostic value of the one or more medical images to the physician for reaching a clinical diagnosis, wherein said estimating comprises mapping the one or more viewing actions to the diagnostic value using an attention metric, wherein the attention metric embodies a set of assumptions about how different viewing behavior of the physician as represented by the viewing actions correlates with different diagnostic values; wherein the above steps are performed for different clinical workflows to obtain a plurality of diagnostic values and a respective plurality of workflow metadata, the method further comprising: - applying a machine learning technique to the plurality of diagnostic values and the plurality of workflow metadata to generate a predictive model, the predictive model being predictive of the diagnostic value of medical images acquired by a particular clinical workflow based on the workflow metadata of the particular clinical workflow; and - optimizing the particular clinical workflow by predicting the diagnostic value of medical images acquired by several variations of the clinical workflow and selecting the variation which yields the best diagnostic value as an optimized variant of the clinical workflow. - obtaining workflow metadata (022, 202, 212) indicative of the clinical workflow, wherein the obtaining the workflow metadata comprises obtaining a system log of a medical apparatus or medical system used in carrying out the clinical workflow; - obtaining a viewer log of an image viewer used by a physician to review the one or more medical images, the viewer log being indicative of one or more viewing actions performed by the physician using the image viewer; - estimating a diagnostic value of the one or more medical images to the physician for reaching a clinical diagnosis, wherein said estimating comprises mapping the one or more viewing actions to the diagnostic value using an attention metric, wherein the attention metric embodies a set of assumptions about how different viewing behavior of the physician as represented by the viewing actions correlates with different diagnostic values; - applying a machine learning technique to the plurality of diagnostic values and the plurality of workflow metadata to generate a predictive model, the predictive model being predictive of the diagnostic value of medical images acquired by a particular clinical workflow based on the workflow metadata of the particular clinical workflow; and - optimizing the particular clinical workflow by predicting the diagnostic value of medical images acquired by several variations of the clinical workflow and selecting the variation which yields the best diagnostic value as an optimized variant of the clinical workflow.