Abstract: Systems and methods are disclosed for providing automated routing of medical data, comprising determining at least one rule corresponding to at least one condition and at least one receiver, receiving medical data and associated medical metadata, determining whether the medical data, the associated medical metadata, and/or associated artificial intelligence processing satisfies the at least one condition of the at least one rule, and upon determining that the at least one condition of the at least one rule is satisfied, providing, from an originating institution, the medical data to the at least one receiver.

Abstract: A computer-implemented method of reviewing digital pathology data may include receiving a digital pathology image into a digital storage device, the digital pathology image being associated with a patient, providing for display the digital pathology image on a display, pairing the digital pathology image with a physical token of the digital pathology image in an interactive system, receiving one or more commands from the interactive system, determining one or more manipulations or modifications to the displayed digital pathology image based on the one or more commands, and providing for display a modified digital pathology image on the display according to the determined one or more manipulations or modifications.

Abstract: Systems and methods are described herein for processing electronic medical images to predict one or more donor recipients for a patient. For example, a digital medical image of the patient may be received, wherein the patient is in need of a transplant. A trained machine learning system may be determined. The digital medical image may be provided into the trained machine learning system, the trained machine learning system determining a patient embedding. Using the patient embedding, a subset of donor recipients may be determined. Based on the subset of donor recipients a recommendation of optimal donors may be determined.

Abstract: Systems and methods are described herein for processing electronic medical images to predict one or more histological morphologies. For example, one or more digital medical images may be received, the one or more digital medical images being of at least one pathology specimen associated with a patient. Patient clinical data for the patient may be received. A trained machine learning system may be determined. The patient clinic data and one or more digital medical images may be provided to the trained machine learning system. A histological morphology prediction of the patient may be determined, using the trained machine learning system. The histological morphology prediction may be output to a user and/or storage.

Abstract: Systems and methods are described herein for processing electronic medical images. The method may include determining, using an automated routine, whether a pathology protocol is accessible; determining a first set of one or more training images, the first set of one or more training images comprising digital medical images annotated utilizing the pathology protocol; and providing the training images to a machine learning model capable of analyzing digital medical images according to the pathology protocol or guideline. The providing may further include determining a starting model, splitting the first set of one or more training images into a training set A and an evaluation set B of digital medical images, fine tuning the starting model with the training set A to determine the machine learning model, evaluating the machine learning model with the training set B, and upon receiving a passing evaluation, saving the determined machine learning model.

Abstract: Systems and methods are described herein for processing electronic medical images. For example, one or more digital medical images of at least one pathology specimen, the pathology specimen being associated with a patient may be received. Additionally, an external designation of the one or more digital medical images may be received. The one or more digital medical images may be provided to one or more machine learning systems, the one or more machine learning systems each having been trained to analyze medical images using one of a plurality of versions of a protocol. The one or more machine learning systems, may determine machine learning system designations for the one or more digital medical images. The external designation may be compared to the machine learning system designations and, based on the comparison, determining whether the external designation matches a predetermined protocol.

Abstract: A computer-implemented method for processing electronic medical images, the method including receiving one or more digital medical images of at least one pathology specimen, the pathology specimen being associated with a patient and receiving one or more search criteria. One or more machine learning systems may be determined based on the one or more search criteria. The one or more machine learning systems may be output to a user, wherein outputting the one or more machine learning system includes applying the one or more machine learning systems to the one or more received medical images, and displaying the one or more digital medical images after the machine learning system performed analysis on the digital medical images. A selection from a user may be received, the selection corresponding to a first machine learning system from the one or more machine learning systems. The first machine learning system may be output.

Abstract: A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.

Abstract: A computer-implemented method for processing electronic medical images, the method including receiving images of at least one pathology specimen, the pathology specimen being associated with a patient. The system may determine, using a machine learning system and based on the electronic medical images, at least one contributing cause of death. The system may provide at least contributing cause of death.

Abstract: A computer-implemented method for processing medical images, the method may include receiving a plurality of medical images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include receiving a gross description, the gross description comprising data about the medical images. The method may next include extracting data from the description. Next, the method may include determining, using a machine learning system, at least one associated location on the medical images for one or more pieces of data extracted. The method may then include outputting a visual indication of the gross description data displayed in relation to the medical images.

Abstract: A computer-implemented method of reviewing digital pathology data may include receiving a digital pathology image into a digital storage device, the digital pathology image being associated with a patient, providing for display the digital pathology image on a display, pairing the digital pathology image with a physical token of the digital pathology image in an interactive system, receiving one or more commands from the interactive system, determining one or more manipulations or modifications to the displayed digital pathology image based on the one or more commands, and providing for display a modified digital pathology image on the display according to the determined one or more manipulations or modifications.

Abstract: Systems and methods are disclosed for providing automated routing of medical data, comprising determining at least one rule corresponding to at least one condition and at least one receiver, receiving medical data and associated medical metadata, determining whether the medical data, the associated medical metadata, and/or associated artificial intelligence processing satisfies the at least one condition of the at least one rule, and upon determining that the at least one condition of the at least one rule is satisfied, providing, from an originating institution, the medical data to the at least one receiver.

Abstract: Systems and methods are disclosed for providing automated routing of medical data, comprising determining at least one rule corresponding to at least one condition and at least one receiver, receiving medical data and associated medical metadata, determining whether the medical data, the associated medical metadata, and/or associated artificial intelligence processing satisfies the at least one condition of the at least one rule, and upon determining that the at least one condition of the at least one rule is satisfied, providing, from an originating institution, the medical data to the at least one receiver.

Abstract: Systems and methods are disclosed for providing automated routing of medical data, comprising determining at least one rule corresponding to at least one condition and at least one receiver, receiving medical data and associated medical metadata, determining whether the medical data, the associated medical metadata, and/or associated artificial intelligence processing satisfies the at least one condition of the at least one rule, and upon determining that the at least one condition of the at least one rule is satisfied, providing, from an originating institution, the medical data to the at least one receiver.