A robot saved a 61-year-old man's life by removing a cancerous tumor from his throat in a first of its kind operation in the U.K. Surgeons at Gloucestershire Royal Hospital used a next-generation robot called Versius to perform the esophagectomy to remove a 2.3-inch tumor from a patient named Martin Nugent. The procedure performed by the robot was a form of minimal access surgery, which creates smaller incisions and reduces the change of complications, scarring and post-operation pain. 'To have been given a second chance to see my grandchildren, my children and my wife has meant so much to me. The team at the GRH saved my life and I'll be forever grateful to them for doing so,' Nugent said. A robot saved a 61-year-old man's life by removing a cancerous tumor from his throat in a first of its kind operation in the U.K. ABOVE: A picture from a different operation using CMR Surgical's Versius robot Surgeons at Gloucestershire Royal Hospital used a next-generation robot called Versius to perform the esophagectomy to remove a 2.3-inch tumor from a patient named Martin Nugent. 'The suite of fully-wristed instruments, combined with enhanced 3D HD vision, give surgeons a high level of accuracy when performing complicated procedural steps or operating in hard to reach areas,' CMR Surgical, the company responsible for robot, says on its website The delicate effort, which involved raising Nugent's stomach and reconnecting it to his esophagus, is credited with giving him a chance to have experiences he didn't think would be possible.

As algorithms and AIs become ever more embedded in people’s lives, there’s also a growing demand for transparency around when an AI is used and what it’s being used for. That means communicating why an AI solution was chosen, how it was designed and developed, on what grounds it was deployed, how it’s monitored and updated, and the conditions under which it may be retired. There are four specific effects of building in transparency: 1) it decreases the risk of error and misuse, 2) it distributes responsibility, 3) it enables internal and external oversight, and 4) it expresses respect for people. Transparency is not an all-or-nothing proposition, however. Companies need to find the right balance with regards to how transparent to be with which stakeholders.

A Cleveland Clinic-backed XR medical visualization startup, MediView XR, Inc., has launched with $4.5 million in funding. The company uses the HoloLens and their custom software to help doctors visualize patient anatomy, and anchor it precisely on their body, giving the doctor, in essence, x-ray specs. The fundamental holographic visualization technology was initially developed at the Lerner Research Institute at the Cleveland Clinic to help surgeons better visualize and plan for the face transplant. Karl West led the team, using a HoloLens to create 3D holographic representations of the donor's skull and other anatomy to assess and refine their surgical plans. Jeffrey Yanof, PhD, created the software.

Though further developments are underway, Google said on April 27 that it has successfully developed new deep learning algorithms that can detect and diagnose diabetic retinopathy, an eye disease which can lead to blindness, as well as locate breast cancer. Lily Peng, product manager of the medical imaging team at Google Research, shared how the US tech giant is using deep learning to train machines to analyze medical images and automatically detect pathological cues, be it swollen blood vessels in the eye or cancerous tumors, during a video conference with the South Korean media hosted by Google Korea. Based on the workings of the human brain, deep learning uses large artificial neural networks -- layers of interconnected nodes -- that rearrange themselves as new information comes in, allowing computers to self-learn without the need for human programming. "Artificial neural networks have been around since the 1960s. But now with more powerful computing power, we can build more layers into the system to handle more complicated tasks with high accuracy," Peng said. "In deep learning, the feature engineering is handled by the computer itself.