Abstract: According to one embodiment, a medical data processing apparatus includes processing circuitry. The processing circuitry determines at least one region of interest for performing processing on a medical image and a degree of importance of the region of interest. The processing circuitry calculates a compression ratio for each of the regions of interest according to the degree of importance.

Abstract: A medical information processing apparatus according to the present embodiment includes a processing circuitry. The processing circuitry is configured to acquire gene therapy information in which a candidate agent that is a therapeutic agent for a gene mutation related to a disease of a patient, an effect of the candidate agent, and ancillary information on the candidate agent are associated with one another. The processing circuitry is configured to determine a priority order of the candidate agent based on the gene therapy information and patient information on the patient. The processing circuitry is configured to present the priority order in association with information representing a relationship between genes.

Abstract: A medical information processing apparatus according to an embodiment includes processing circuitry configured to obtain a plurality of illness candidates; collect information serving as the evidence for determining the illness of the patient from among the plurality of illness candidates, and obtain a score for each illness candidate based on that information; identify, based on the scores, an examination candidate meant for supporting the diagnosis of the patient; and perform output based on the examination candidate.

Abstract: A medical information processing apparatus according to an embodiment includes a processing circuitry. The processing circuitry is configured: to register first relevance information relevant to a first trained model to be newly generated; to calculate, with respect to each of a plurality of second trained models being existing trained models, a similarity degree between second relevance information relevant to the second trained model and the first relevance information; to calculate a data quantity required to generate the first trained model with respect to each of the plurality of second trained models, on the basis of the similarity degrees each corresponding to a different one of the plurality of second trained models; and to output the data quantity required to generate the first trained model with respect to each of the plurality of second trained models.