Artificial intelligence is allowing surgical robots to perform some tasks with limited direct control from surgeons. However, patients’ willingness to undergo surgery involving semi-autonomous robots may influence how widely these technologies are adopted. This study explored patients’ views about the ethical concerns surrounding semi-autonomous robot-assisted surgery and how these concerns affect their willingness to undergo such procedures. Researchers interviewed 50 adults who had undergone surgery within the previous three years, either without a robot or with a robot that was not autonomous. Overall, 56% said they would be willing to undergo semi-autonomous robot-assisted surgery, although willingness depended on which parts of the procedure the robot controlled. Four main factors shaped participants’ views: having enough information to make an informed decision, concerns about new risks introduced by robotic autonomy, trust and the relationship between patients and surgeons, and the potential benefits of robotic technology. Patients also considered the balance of control between the surgeon and robot important. These findings suggest that many patients may be open to semi-autonomous robot-assisted surgery, but acceptance depends on clear informed consent, confidence in safety, trust in surgeons, and understanding the technology’s potential benefits.

Fig 2 . Percent of participants by their willingness to undergo semi-autonomous robot-assisted surgery and by the type of control the robot would have over the different types of surgical processes

Health data are increasingly collected and shared across healthcare, research, and artificial intelligence (AI) applications, creating new opportunities for medical discovery and clinical decision-making. At the same time, these uses raise growing concerns about privacy, security, and trust. This review examines risks and protections throughout the health data life cycle, from their initial collection and use in healthcare to their later use in research and AI development. We discuss how regulations, organizational practices, and technology influence data protection, as well as emerging risks such as unintended disclosure of sensitive information through AI tools. We review methods for protecting health data, including controlling and monitoring access, assessing the risk that individuals could be re-identified, using statistical privacy techniques such as differential privacy, and generating synthetic data that resemble real data without directly representing individual patients. We also discuss approaches that allow organizations to collaborate without directly sharing sensitive data, including federated learning and secure cryptographic methods. These approaches involve important trade-offs between protecting privacy and preserving the usefulness of data. Overall, greater standardization, transparency, and practical guidance are needed to strengthen privacy and trust while supporting responsible use of health data in healthcare, research, and AI.

Figure 1 Simplified representation of different stages in the health data flow with potential risks and mitigation strategies. In the data-generation stage, health data are generated by individuals (e.g., patients). In the primary use stage (pink), data are collected for care by healthcare organizations or third-party systems. In the secondary use stage (yellow), data are shared and analyzed to accelerate knowledge discovery.

Ashwin, T. S.; Sanda, Nihar; Coursey, Austin; Gupta, Vaibhav; Biswas, Gautam. (2026).��.��Proceedings of the ACM Symposium on Applied Computing, 87–94.��

Franklin, Jacob; Chu, Ling; McDonald, Samuel A.; Turer, Robert W.; Webb, Janet; Steitz, Bryan D.; Yi, Haoyang; Chen, Qingxia; McCoy, Allison B.; Nall, Carolynn; Chen, Catherine; Rousseau, Justin F.; Lehmann, Christoph U.; Willett, Duwayne L.; Kumah-Crystal, Yaa A.; Mize, Dara E. (2026).��.��Discover Artificial Intelligence, 6(1), 550.��

This study addresses the challenge of��automatic polyp segmentation, the process of identifying and outlining polyps in colonoscopy images. While many artificial intelligence (AI) models perform well on the data they were trained on, they often struggle when tested on images from different hospitals or datasets because polyps can vary widely in size, shape, color, and appearance. Differences in image quality, such as glare from reflected light, blur, fluid, and changes in lighting during colonoscopy, add to this challenge. To improve performance across different datasets, the researchers developed��HAT-SAM3, an adaptation of the��Segment Anything Model 3 (SAM 3), a large pre-trained AI model originally designed for image segmentation. HAT-SAM3 uses a training technique called��highlight-aware training (HAT), which helps the model better handle bright reflections commonly seen in endoscopy images without changing how the model operates during testing. When evaluated on five publicly available datasets, HAT-SAM3 achieved an average��Dice score(a measure of how closely the model’s predicted outlines match the true polyp boundaries) of 0.884 and produced the best reported results on four of the five datasets. In additional testing, where the model was trained on one dataset and evaluated on three completely separate datasets without further training, HAT-SAM3 consistently outperformed the strongest previously reported method. These results suggest that adapting a pre-trained foundation model with endoscopy-specific training techniques can improve the accuracy and reliability of automated polyp segmentation across a wide range of clinical datasets. The researchers have also made their code publicly available to support future research.

Fig 1: Motivation of HAT-SAM3. A task-specific polyp segmenter can perform well when training and test images come from the same visual distribution, but degrade under cross-domain changes in illumination, blur, specular reflection, and polyp appearance. HAT-SAM3 uses a broad segmentation prior with endoscopy-aware adaptation to improve cross-domain polyp segmentation

Research on artificial intelligence tools that help clinicians make decisions has had mixed results: sometimes these tools improve decision-making, and sometimes they do not, and it is not always clear why. This review looked at what factors affect how well AI-based clinical decision-support systems, or AI-CDS, work in medicine. The authors searched three medical databases and found 45 relevant studies out of 5,850 screened articles. They focused on how both clinician factors and technology design features influenced three things: how clinicians feel about AI, whether they accept AI recommendations, and how well they perform when using AI. The review found that experienced clinicians may gain less from AI support than less experienced clinicians, although the results were not consistent across studies. It also found that explainable AI, meaning AI that gives reasons for its suggestions, can increase trust, but it may also lead clinicians to trust incorrect recommendations too much, which can hurt performance when humans and AI work together. Clinicians’ existing attitudes toward AI also influenced whether they accepted its advice. Overall, the review suggests that future research should focus on “appropriate trust,” meaning clinicians should rely on AI only when the advice is actually trustworthy, rather than simply trying to increase trust in AI overall.

Figure 1.

PRISMA diagram for study inclusion.

Agentic AI��refers to systems that can set their own goals, adapt to new situations, and improve over time through experience. This study explores how such systems can learn more efficiently by sharing knowledge with one another, instead of learning everything from scratch. In particular, it looks at how an AI agent can decide��what knowledge to reuse, which other agents to learn from, and how to incorporate that knowledge��into its own decision-making process (often called a��policy, or strategy for choosing actions).

The researchers introduce a new method called��MOSAIC (Modular Sharing and Composition in Collective Learning). This approach allows agents to compare tasks using mathematical representations, select useful knowledge from others based on performance and similarity, and integrate it using flexible, modular neural network components. In simple terms, agents can “borrow” and adapt pieces of what others have already learned.

The results show that agents using MOSAIC learn faster and perform better than those learning alone or sharing information indiscriminately. In some cases, they can even solve problems that individual agents cannot. The study also finds that targeted, selective sharing reduces confusion between tasks and leads to a kind of��self-organization, where agents working on easier problems help others tackle more complex ones. Overall, this work highlights the potential of collaborative learning strategies to make AI systems more efficient and adaptable.

Skyler Grandel; Scott Thomas Andersen; Yu Huang; Kevin Leach (2026).��.��ACM Transactions on Software Engineering and Methodology, 35(3), Article 82.��

This study explores a way to automatically monitor how laboratory tasks are performed, focusing on pipetting—a common technique where small amounts of liquid are transferred using a pipette. Ensuring that such procedures are done correctly is important for safety and for producing reliable results, but it is difficult to track in real time because it involves complex hand movements, tool use, and multiple steps that vary between users. To address this, the researchers developed a vision-based artificial intelligence system that uses video recordings instead of physical sensors. The system first applies a YOLO-based model (a type of object detection algorithm) to identify human body positions and interactions with the pipette. It then uses a bidirectional long short-term memory (LSTM) network, a type of deep learning model designed to analyze sequences over time, to understand how the actions unfold step by step.

The results show that this approach can successfully distinguish between correct (standard) and incorrect (non-standard) pipetting behaviors, including different types of errors, and performs better than methods that analyze images one frame at a time without considering motion over time. Overall, the study demonstrates that AI systems using video analysis can provide a practical, non-contact way to monitor laboratory techniques, potentially improving quality control and extending to other manual procedures in scientific labs.

Figure 1.��Representative incorrect pipetting behaviors and key challenges for vision-based QA in chemical laboratory environments.

This study looks at how measurements of the spinal cord—such as its��cross-sectional area��(the size of the cord when viewed in a slice)—can be used as important indicators (biomarkers) for diagnosing and tracking neurological diseases like multiple sclerosis or spinal cord compression. Modern artificial intelligence methods can automatically identify and outline (segment) the spinal cord in MRI scans, but it is unclear whether these measurements stay consistent as models are updated with new data over time. This consistency is especially important when building “normal” reference values from healthy individuals.

To address this, the researchers developed a spinal cord segmentation model trained on a large and diverse dataset collected from 75 sites and over 1,600 participants, covering different MRI types and various spinal cord conditions. They also created a “lifelong learning” system that continuously monitors changes in measurements (called��morphometric drift) whenever the model is updated. This system automatically runs through a workflow (via GitHub Actions, an automated coding tool) to track how measurements evolve over time.

The results showed that the new model performs very well, accurately identifying the spinal cord even in challenging cases such as severe compression or tissue damage, with a high Dice score (a measure of how closely the model’s segmentation matches the true anatomy) of 0.95. The monitoring system also proved useful for quickly detecting any changes in measurements between model versions. Importantly, the study found that updates to the model caused only minimal shifts in spinal cord measurements, meaning the results remain stable and reliable. This allowed the researchers to safely update an existing database of normal spinal cord measurements. Overall, this work provides a reliable and transparent way to maintain consistency in AI-based medical measurements as models evolve.

Fig 1

Overview of the dataset and image characteristics. Representative axial slices of nine contrasts and the total of images used for each contrast in brackets, the orientation (axial/sagittal) along with the median resolution of images. The respective doughnut chart illustrates the proportion of clinical status among the scanned participants, including healthy controls (HC), patients with radiologically isolated syndrome (RIS), patients with multiple sclerosis (MS), and their different phenotypes, including primary progressive (PPMS) and relapsing-remitting (RRMS), patients with amyotrophic lateral sclerosis (ALS), patients with neuromyelitis optica spectrum disorder (NMOSD), pre-decompression acute traumatic SCI (AcuteSCI), post-decompression traumatic spinal cord injury (SCI), degenerative cervical myelopathy (DCM), and syringomyelia (SYR; not shown). Labels indicate the phenotype associated with the patient, with their respective colors shared across contrast sets.