Steerable needles are minimally invasive devices that enable novel medical procedures by following curved paths to avoid critical anatomical obstacles. Planning algorithms can be used to find a steerable needle motion plan to a target. Deployment typically consists of a physician manually inserting the steerable needle into tissue at the motion plan's start pose and handing off control to a robot, which then autonomously steers it to the target along the plan. The handoff between human and robot is critical for procedure success, as even small deviations from the start pose change the steerable needle's workspace and there is no guarantee that the target will still be reachable. We introduce a metric that evaluates the robustness to such start pose deviations. When measuring this robustness to deviations, we consider the tradeoff between being robust to changes in position versus changes in orientation. We evaluate our metric through simulation in an abstract, a liver, and a lung planning scenario. Our evaluation shows that our metric can be combined with different motion planners and that it efficiently determines large, safe start regions.

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.

The prospect of using autonomous robots to enhance the capabilities of physicians and enable novel procedures has led to considerable efforts in developing medical robots and incorporating autonomous capabilities. Motion planning is a core component for any such system working in an environment that demands near perfect levels of safety, reliability, and precision. Despite the extensive and promising work that has gone into developing motion planners for medical robots, a standardized and clinically-meaningful way to compare existing algorithms and evaluate novel planners and robots is not well established. We present the Medical Motion Planning Dataset (Med-MPD), a publicly-available dataset of real clinical scenarios in various organs for the purpose of evaluating motion planners for minimally-invasive medical robots. Our goal is that this dataset serve as a first step towards creating a larger robust medical motion planning benchmark framework, advance research into medical motion planners, and lift some of the burden of generating medical evaluation data.

The presence of artificial intelligence in the federal workforce is poised to expand, with officials emphasizing the human component behind automation and machine learning technologies. Officials including Gil Alterovitz, the Veterans' Affairs National Artificial Intelligence Institute director, and Martin Stanley, the branch chief of Strategic Technology at the Cybersecurity and Infrastructure Security Agency, spoke during a Thursday panel and discussed digitization within their respective agencies. Alterovitz said that VA leadership has opened up new data scientist positions to serve as subject matter experts across the government. "We've been working toward building pathways toward developing and assessing that AI knowledge," he said. "We're working with a number of other agencies and really the idea there is to build that pipeline of talent with AI knowledge both from outside government [and] inside the government so that the result of that would be an agile and responsive federal workforce equipped with the necessary competencies for AI." Alterovitz also discussed the ethical parameters the VA has in place for its usage of automated technology.

Interest in an artificial intelligence "to-go" delivery model is building with more than a dozen Department of Veterans Affairs sites looking to pilot modules, the agency's head of AI said Thursday. VA developed the initial module to assist its medical centers with COVID-19 individual risk prediction, but its hundreds of centers and thousands of facilities have other uses for the statistical models being tested, said Gil Alterovitz, VA's director of AI. Additional use cases haven't been chosen, but AI models will be packaged as embeddable software add-ons for rapid deployment based on the original. "We're now using that to generalize and essentially created a new platform so that artificial intelligence research and development can be added as modules in the future," Alterovitz said during day three of FedTalks, presented by FedScoop. Once the AI technology and health application have been vetted, any medical center will be able to access a module when VA shares a secure, internal weblink.

Researchers at the Tampa veterans' hospital are training computers to diagnose cancer. It's one example of how the Department of Veterans Affairs is expanding artificial intelligence development. Inside a laboratory at the James A. Haley Veterans' Hospital in Tampa, Fla., machines are rapidly processing tubes of patients' body fluids and tissue samples. Pathologists examine those samples under microscopes to spot signs of cancer and other diseases. But distinguishing certain features about a cancer cell can be difficult, so Drs.

The Veterans Affairs Department's nascent National Artificial Intelligence Institute is focusing on hammering out policies and streamlining its processes so it can share data with partners in a speedy way. After its initial launch late last year, the agency's inaugural artificial intelligence director and lead of the center Dr. Gil Alterovitz shared few details with Nextgov about its ultimate aims, but at an event in Washington Wednesday, he expanded on the center's early intentions and efforts. "We are at that time in history where human intelligence at some point will intersect with artificial intelligence," Alterovitz said at the ACT-IAC's second intelligent automation and AI forum. "And so it's a really special time for us to learn about it." The AI director said the intersection between human intelligence and specific areas where computational artificial intelligence will meet is inching closer to reality by the day.

The Veterans Affairs Department recently launched a National Artificial Intelligence Institute to coordinate and advance strategic vet-focused research and development efforts to harness the budding technology. "VA has a unique opportunity to be a leader in artificial intelligence," Secretary Robert Wilkie said in a statement. "VA's artificial intelligence institute will usher in new capabilities and opportunities that will improve health outcomes for our nation's heroes." Home to America's largest integrated health care system, the VA trains more doctors and nurses than any other entity in the nation and also houses the largest genomic knowledge base linked to health care information in the world. Throughout 2019, the agency unveiled a variety of deliberate investments and projects to leverage artificial intelligence to better meet veterans' needs.

The Department of Veterans Affairs (VA) wants to become a leader in artificial intelligence and launched a new national institute to spur research and development in the space. The VA's new National Artificial Intelligence Institute (NAII) is incorporating input from veterans and its partners across federal agencies, industry, nonprofits, and academia to prioritize AI R&D to improve veterans' health and public health initiatives, the VA said in a press release. "VA has a unique opportunity to be a leader in artificial intelligence," VA Secretary Robert Wilkie said in a statement. "VA's artificial intelligence institute will usher in new capabilities and opportunities that will improve health outcomes for our nation's heroes." RELATED: VA taps Google's DeepMind to predict patient deterioration For its AI projects, the VA plans to leverage its integrated health care system and the healthcare data it has amassed, thanks to its Million Veteran Program.

Editor's Note: This edition of Morning eHealth is published Mondays, Wednesdays and Fridays at 10 a.m. POLITICO Pro eHealth subscribers hold exclusive early access to the newsletter each morning at 6 a.m. Manage the high volume of regulatory information and track new changes and comments all in one, easy-to-use platform. I'm not able to schedule diagnostic tests online via my EHR platform. It's FRIDAY at Morning eHealth, where your author has yet to see a Baby Yoda/health IT crossover meme, which is perhaps for the best.