The rise of Large Language Models (LLMs) has impacted research in robotics and automation. While progress has been made in integrating LLMs into general robotics tasks, a noticeable void persists in their adoption in more specific domains such as surgery, where critical factors such as reasoning, explainability, and safety are paramount. Achieving autonomy in robotic surgery, which entails the ability to reason and adapt to changes in the environment, remains a significant challenge. In this work, we propose a multi-modal LLM integration in robot-assisted surgery for autonomous blood suction. The reasoning and prioritization are delegated to the higher-level task-planning LLM, and the motion planning and execution are handled by the lower-level deep reinforcement learning model, creating a distributed agency between the two components. As surgical operations are highly dynamic and may encounter unforeseen circumstances, blood clots and active bleeding were introduced to influence decision-making. Results showed that using a multi-modal LLM as a higher-level reasoning unit can account for these surgical complexities to achieve a level of reasoning previously unattainable in robot-assisted surgeries. These findings demonstrate the potential of multi-modal LLMs to significantly enhance contextual understanding and decision-making in robotic-assisted surgeries, marking a step toward autonomous surgical systems.

An invention by scientists in the Netherlands aims to break up blood clots without surgery or drugs. Blood clots are a serious health problem that can cause strokes, heart attacks and even death. Some blood clots can be removed by doctors using a flexible tool that goes inside the affected vein or artery, but others are too hard to reach. What if there was a way to break up those clots without surgery or drugs? CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Scientists have created tiny robots that can swim through your blood vessels and drill into the clots.

Artificial Intelligence (AI) is already helping doctors and medical professionals in a variety of ways. AI can help diagnose diseases, identify genetic risk factors, and even predict how patients will respond to certain drugs. But could AI be used to improve radiology? In the early days of radiography, radiology was a part of medicine. Doctors used x-rays to diagnose and treat a large variety of illnesses, from arthritis to cancer.

Artificial Intelligence (AI) is already helping doctors and medical professionals in a variety of ways. AI can help diagnose diseases, identify genetic risk factors, and even predict how patients will respond to certain drugs. But could AI be used to improve radiology? In the early days of radiography, radiology was a part of medicine. Doctors used x-rays to diagnose and treat a large variety of illnesses, from arthritis to cancer.

Don't worry, yes, there are even more Musk machinations, but first let's broach something a little different -- and possibly lifesaving. A team of MIT engineers is developing a telerobotic system for neurosurgeons. It unveiled a robotic arm that doctors can control remotely using a modified joystick to treat stroke patients. The arm has a magnet attached to its wrist, and surgeons can adjust its orientation to guide a magnetic wire through the patient's arteries and vessels to remove blood clots in the brain. Like in-person procedures, surgeons will have to rely on live imaging to get to the blood clot, but the machine means they don't have to be physically with the patient.

Remote robotic-assisted surgery is far from new, with various educational and research institutions developing machines doctors can control from other locations over the years. There hasn't been a lot of movement on that front when it comes to endovascular treatments for stroke patients, which is why a team of MIT engineers has been developing a telerobotic system surgeons can use over the past few years. The team, which has published its paper in Science Robotics, has now presented a robotic arm that doctors can control remotely using a modified joystick to treat stroke patients. That arm has a magnet attached to its wrist, and surgeons can adjust its orientation to guide a magnetic wire through the patient's arteries and vessels in order to remove blood clots in their brain. Similar to in-person procedures, surgeons will have to rely on live imaging to get to the blood clot, except the machine will allow them to treat patients not physically in the room with them.

Blood clots might be soon be cleared by using microscopic robots with screw-like propellers that swim through blood vessels bearing blockage-busting drugs. Developed by engineers from the Chinese University of Hong Kong, the microrobot design was inspired by the tails of bacteria like E. coli. The tiny robot and its propeller are driven by the application of an external magnetic field -- and it is capable of moving with and against the direction of the blood flow. Tests in a fake vein filled with pig's blood showed the robot makes its clot-dissolving'tissue plasminogen activator' cargo five times more effective than the drug alone. The robot's rotor, the team said, may help to circulate the drug around the blockage site -- better breaking up the clots and reducing the risk of large fragments.

Pulmonary embolisms are dangerous, lung-clogging blot clots. In a pilot study, scientists at the Icahn School of Medicine at Mount Sinai showed for the first time that artificial intelligence (AI) algorithms can detect signs of these clots in electrocardiograms (EKGs), a finding which may one day help doctors with screening. The results published in the European Heart Journal – Digital Health suggested that new machine learning algorithms, which are designed to exploit a combination of EKG and electronic health record (EHR) data, may be more effective than currently used screening tests at determining whether moderate- to high-risk patients actually have pulmonary embolisms. The study was led by Sulaiman S. Somani, MD, a former medical student in the lab of Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences and a member of the Hasso Plattner Institute for Digital Health at Mount Sinai. Pulmonary embolisms happen when deep vein blood clots, usually formed in the legs or arms, break away and clog lung arteries. These clots can be lethal or cause long-term lung damage.

In this paper, we explore the ability of sequence to sequence models to perform cross-domain reasoning. Towards this, we present a prompt-template-filling approach to enable sequence to sequence models to perform cross-domain reasoning. We also present a case-study with commonsense and health and well-being domains, where we study how prompt-template-filling enables pretrained sequence to sequence models across domains. Our experiments across several pretrained encoder-decoder models show that cross-domain reasoning is challenging for current models. We also show an in-depth error analysis and avenues for future research for reasoning across domains

Segmentation is robust throughout compression. The venous region is evaluated for full compressibility to rule out DVT. Device: Clarius L7 (2017). The team of researchers aims to diagnose deep vein thrombosis (DVT) as quickly and effectively as traditional radiologist-interpreted diagnostic scans, reduce long patient waiting lists, and avoid patients. Receive medication unnecessarily to treat DVT when they do not have it. DVT is one of the most commonly formed blood clots in the legs, causing swelling, pain, and discomfort.