Abstract: Image processing engines can be utilized to inject studies into other commercial or independently-developed peer review systems which are designed to review the medical findings identified by a set of physicians. Image processing engines detect, confirm or verify findings by physicians or other engines, where the engines operate as peer reviewers. The engines can prospectively “learn” from the feedback when these images are reviewed by the physicians during diagnostic interpretation creating a closed-loop quality assurance process and fostering a community platform approach to engine development which is supported by the security, governance, access control, regulatory compliance and other features of the Peer Review System. Utilizing machine learning based on the data collected from peer review, the Peer Review System can adapt and improve its performance as well as the measured performance of the physicians using the system for diagnostic interpretation.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms that take into account stored preferences for a user. A client viewer presents the findings to the user. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information. A machine learning engine receives the tracking information and based on the adjustments made to the findings by the user adjusts the stored preferences for the user.

Abstract: An artificial intelligence findings system includes a findings engine that receives medical image data and generates findings based on the medical image data and image interpretation algorithms that take into account stored preferences for a user. A client viewer presents the findings to the user. An adjustment engine allows the user to adjust the findings to produce a report. A tracking module tracks findings and adjustments made to the findings by the user when producing the report. The tracking module produces tracking information.

Abstract: Image processing engines can be utilized to inject studies into other commercial or independently-developed peer review systems which are designed to review the medical findings identified by a set of physicians. Image processing engines detect, confirm or verify findings by physicians or other engines, where the engines operate as peer reviewers. The engines can prospectively “learn” from the feedback when these images are reviewed by the physicians during diagnostic interpretation creating a closed-loop quality assurance process and fostering a community platform approach to engine development which is supported by the security, governance, access control, regulatory compliance and other features of the Peer Review System. Utilizing machine learning based on the data collected from peer review, the Peer Review System can adapt and improve its performance as well as the measured performance of the physicians using the system for diagnostic interpretation.

Abstract: A syringe can be used to receive fluid from a patient. The syringe can include a syringe body, a first lumen disposed within an internal space of the syringe body, a second lumen disposed within the internal space of the syringe body, and a plunger slideably received along an internal surface of the second lumen. The syringe can also include a stem elongate along a first direction, a handle coupled to the stem, and a needle coupled to the syringe body. The syringe can be configured such that the plunger is moved through the second lumen along a first direction and the fluid is received into the first lumen along a second direction that is opposite the first direction.