The invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. These methods can leverage domain knowledge and clinical expertise with cognitive collaboration, augmented medical intelligence and cybernetic workflow streams for learning health care systems. The invention enables multimodal cognitive communications, collaboration, consultation and instruction between and among cognitive collaborants, including heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms. It provides for both synchronous and asynchronous cognitive collaboration with multichannel, multiplexed imagery data streams during various stages of medical disease and injury management—detection, diagnosis, prognosis, treatment, measurement and monitoring, as well as resource utilization and outcomes reporting. The invention acquires both live stream and archived medical imagery data from network-connected medical devices, cameras, signals, sensors and imagery data repositories, as well as multiomic data sets from structured reports and clinical documents. It enables cognitive curation, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized medical intelligence. The invention augments packetized medical intelligence through recursive cognitive enrichment, including multimodal annotation and [semantic] metadata tagging with resources consumed and outcomes delivered. Augmented medical intelligence can be saved and stored in multiple formats, as well as retrieved from standards-based repositories. The invention provides neurosynaptic network connectivity for medical images and video with multi-channel, multiplexed gateway streamer servers that can be configured to support workflow orchestration across the enterprise—on platform, federated or cloud data architectures, including ecosystem partners. It also supports novel methods for managing augmented medical intelligence with networked metadata repositories [inclduing imagery data streams annotated with semantic metadata]. The invention helps prepare streaming imagery data for cognitive enterprise imaging. It can be incorporate and combine various machine learning techniques [e.g., deep, reinforcement and transfer learning, convolutional neural networks and NLP] to assist in curating, annotating and tagging diagnostic, procedural and evidentiary medical imaging. It also supports real-time, intraoperative imaging analytics for robotic-assisted surgery, as well as other imagery guided interventions. The invention facilitates collaborative precision medicine, and other clinical initiatives designed to reduce the cost of care, with precision diagnosis [e.g., integrated in vivo, in vitro, in silico] and precision targeted treatment [e.g., precision dosing, theranostics, computer-assited surgery]. Cybernetic workflow streams—cognitive communications, collaboration, consultation and instruction with augmented medical intelligence—enable care delivery teams of medical minds and machines to ‘deliver the right care, for the right patient, at the right time, in the right place’ - and deliver that care faster, smarter, safer, more precisely, cheaper and better.