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 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, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs. The invention enables cognitive curation, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized medical intelligence. It can augment 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 can incorporate and combine various machine learning techniques [e.g., deep, reinforcement and transfer learning, convolutional and recurrent neural networks, LSTM 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 ima
