Electrosurgery is a surgical technique that can improve tissue cutting by reducing cutting force and bleeding. However, electrosurgery adds a risk of thermal injury to surrounding tissue. Expert surgeons estimate desirable cutting velocities based on experience but have no quantifiable reference to indicate if a particular velocity is optimal. Furthermore, prior demonstrations of autonomous electrosurgery have primarily used constant tool velocity, which is not robust to changes in electrosurgical tissue characteristics, power settings, or tool type. Thermal imaging feedback provides information that can be used to reduce thermal injury while balancing cutting force by controlling tool velocity. We introduce Thermography for Electrosurgical Rate Modulation via Optimization (ThERMO) to autonomously reduce thermal injury while balancing cutting force by intelligently controlling tool velocity. We demonstrate ThERMO in tissue phantoms and compare its performance to the constant velocity approach. Overall, ThERMO improves cut success rate by a factor of three and can reduce peak cutting force by a factor of two. ThERMO responds to varying environmental disturbances, reduces damage to tissue, and completes cutting tasks that would otherwise result in catastrophic failure for the constant velocity approach.

An AI-powered robot was able to remove a gall bladder from a dead pig in what researchers claim is the first realistic surgery by a machine with almost no human intervention. The robot is powered by a two-tier AI system trained on 17 hours of video encompassing 16,000 motions made in operations by human surgeons. When put to work, the first layer of the AI system watches video from an endoscope monitoring the surgery and issues plain-language instructions, such as "clip the second duct", while the second AI layer turns each instruction into three-dimensional tool motions. In all, the gall bladder surgery required 17 separate tasks. The robotic system performed the operation eight times, achieving 100 per cent success in all of the tasks.

Anthropic's first developer conference kicked off in San Francisco on Thursday, and while the rest of the industry races toward artificial general intelligence, at Anthropic the goal of the year is deploying a "virtual collaborator" in the form of an autonomous AI agent. "We're all going to have to contend with the idea that everything you do is eventually going to be done by AI systems," Anthropic CEO Dario Amodei said in a press briefing. As roughly 500 attendees munched breakfast sandwiches with an abnormal amount of arugula, and Anthropic staffers milled about in company-issued baseball caps, Amodei took the stage with his chief product officer, Mike Krieger. "When do you think there will be the first billion-dollar company with one human employee?" Amodei, wearing a light-gray jacket and a pair of Brooks running shoes, replied without skipping a beat: "2026."

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels.

Robots can already mimic surgeons to a certain degree, but training them to do so often involves complex programming and time-consuming trial-and-error. Now, for the first time, a machine successfully learned to replicate fundamental operation tasks after simply analyzing video footage of medical experts. But before it gets to work on human patients, the tiny robotic arms practiced on a pork loin. Doctors have increasingly integrated the da Vinci Surgical System into an array of procedures since the device's debut in 2000. The small pair of robotic arms ending in tweezer-like graspers are already used in prostatectomies, cardiac valve repairs, as well as renal and gynecologic operations.

The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance equity and reduce bias. However, there is an opportunity to also improve the explainability of AI by leveraging best practices of social epidemiology and health equity to help us develop hypotheses for associations found. In this paper, we focus on explainable AI (XAI) and describe a framework for interdisciplinary expert panel review to discuss and critically assess AI model explanations from multiple perspectives and identify areas of bias and directions for future research. We emphasize the importance of the interdisciplinary expert panel to produce more accurate, equitable interpretations which are historically and contextually informed. Interdisciplinary panel discussions can help reduce bias, identify potential confounders, and identify opportunities for additional research where there are gaps in the literature. In turn, these insights can suggest opportunities for AI model improvement.

Today, Artifact is taking another jump on the generative-AI rocket ship in an attempt to address an annoying problem--clickbaity headlines. The app already offers a way for users to flag clickbait stories, and if multiple people tag an article, Artifact won't spread it. But, Systrom explains, sometimes the problem isn't with the story but the headline. It might promise too much, or mislead, or lure the reader into clicking just to find some information that's held back from the headline. From the publisher's viewpoint, winning more clicks is a big plus--but it's frustrating to users, who might feel they have been manipulated.

After a few years of staying mostly under the radar, Instagram co-founders Kevin Systrom and Mike Krieger are back with a new project. It's an app called Artifact, a name Systrom told Platformer's Casey Newton is designed to evoke the project's three tenants: "articles, facts and artificial intelligence." In short, it's a news aggregation app driven by a TikTok-like recommendation algorithm. When you first launch Artifact, you'll see a central feed populated by stories from publications like The New York Times. As you read more articles, the app will begin personalizing your feed.

In a high-tech lab on Johns Hopkins University's Homewood campus in Baltimore, engineers have been building a robot that may be able to stitch back together the broken vessels in your belly and at some point maybe your brain, no doctor needed. The robot has a high-tech camera on one arm and a high-tech sewing machine on a second arm. "It's like park assist in a car," said Axel Krieger, an assistant professor of mechanical engineering in Hopkins' Whiting School of Engineering. This kind of suturing is performed more than a million times a year in surgeries around the country, said Krieger, part of a team developing the robot and senior author on a recent paper describing the technology in science robotics. The goal is to develop in the next several years a robot that makes the intricate and delicate work of suturing more consistent.

A robot has performed laparoscopic surgery on the soft tissue of a pig without the guiding hand of a human--a significant step in robotics toward fully automated surgery on humans. Designed by a team of Johns Hopkins University researchers, the Smart Tissue Autonomous Robot (STAR) is described today in Science Robotics. "Our findings show that we can automate one of the most intricate and delicate tasks in surgery: the reconnection of two ends of an intestine. The STAR performed the procedure in four animals and it produced significantly better results than humans performing the same procedure," said senior author Axel Krieger, an assistant professor of mechanical engineering at Johns Hopkins' Whiting School of Engineering. The robot excelled at intestinal anastomosis, a procedure that requires a high level of repetitive motion and precision.