Abstract: There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.

Abstract: A processor is configured to: divide a target image into a plurality of first regions through a first division; divide the target image into a plurality of second regions through a second division different from the first division; derive a feature vector that represents at least a feature of each of the second regions for each of the first regions; derive a determination result for a target object included in the target image based on the feature vector; specify, among elements of the feature vector, an influential element that affects the determination result; and specify an influential region that affects the determination result in the target image based on the influential element.

Abstract: Provided are an information processing device, a program, a trained model, a diagnostic support device, a learning device, and a prediction model generation method that can perform prediction with high accuracy using images. An information processing device includes: an information acquisition unit that receives an input of image data and non-image data related to a target matter; and a prediction unit that predicts an aspect related to the matter at a time different from a time when the image data is captured on the basis of the image data and the non-image data input through the information acquisition unit.

Abstract: There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.