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.

Researchers evaluated whether artificial intelligence (AI) could help doctors better judge the cancer risk of indeterminate pulmonary nodules, which are small lung spots seen on a CT scan whose cause is not yet clear. These nodules are becoming more common as lung cancer screening and CT imaging are used more often. The team built a decision model to compare two approaches: clinician evaluation alone versus clinician evaluation supported by AI-based radiomics, a method that analyzes patterns in imaging data. They asked whether the AI approach would improve health outcomes and whether it would be worth the extra cost from a payer’s perspective over a patient’s lifetime. In their base case—a 60-year-old patient with a 1.1 cm nodule and a fairly high chance of cancer (65%)—AI support led to a small gain of 0.03 life-years and was cost-effective, with an incremental cost-effectiveness ratio of $4,485 per life-year gained. However, when the chance that the nodule was cancer was very low, below 5%, the AI approach no longer met a typical cost-effectiveness threshold of $100,000 per life-year gained. Overall, the study suggests that AI-assisted nodule assessment is cost-effective in settings where the likelihood of cancer is greater than 5%.

Fig 1.��Decision Model Structure.

The decision tree structure models the risk-stratification of an indeterminate pulmonary nodule utilizing artificial intelligence-assistance compared to the clinician alone. Repeated portions of the model have been collapsed into subtrees (A-G) for readability, each of which represents a diagnostic or management pathway that appears in various parts of the model (‘A’ = surveillance; ‘B’ = PET-CT evaluation; C = minimally invasive surgical (MIS) lobectomy; ‘D’ = low-risk surveillance; ‘E’ = intermediate-risk; ‘F’ = MIS wedge resection; ‘G’ = initial risk classification).

Recent progress in multimodal artificial intelligence has shown that spatial transcriptomics data, which measure where genes are active within tissue, can potentially be generated from standard histology images, reducing the cost and time required for specialized experiments. However, newer spatial transcriptomics platforms such as Visium HD operate at very high resolution, down to about 8 micrometers, which creates major computational challenges. At this scale, traditional methods that predict gene expression one spot at a time become slow, unstable, and poorly suited to the extreme sparsity of gene expression, where many genes have very low or zero signal. To address this, the authors developed Img2ST Net, a high-resolution framework that predicts spatial transcriptomics data from histology images using a fully convolutional neural network, meaning the model generates dense gene expression maps all at once rather than sequentially. The method represents high-resolution spatial transcriptomics as groups of small regions called super pixels and reframes the task as an image generation problem with hundreds or thousands of output channels, each corresponding to a gene. This approach improves efficiency and better preserves spatial structure in the tissue. To evaluate performance under sparse expression conditions, the authors also introduced SSIM ST, a structural similarity-based metric designed specifically for high-resolution spatial transcriptomics. Testing on public breast and colorectal cancer Visium HD datasets at 8 and 16 micrometer resolution showed that Img2ST Net outperformed existing methods in both prediction accuracy and spatial coherence, while reducing training time by up to 28 times compared with spot-based approaches. Additional analyses showed that contrastive learning further improved spatial fidelity. Overall, this work provides a scalable and biologically meaningful solution for predicting high-resolution spatial transcriptomics data and supports future large-scale and resolution-aware spatial omics modeling.

Fig.��1

Modeling paradigm for ST prediction. (a)��Conventional patch-to-spot regression manner for Visium ST data: each WSI contains hundreds of��55μm��spots for the ST slide. A separate gene expression vector is predicted for each spot from its corresponding image patch. (b)��Our proposed image-to-image prediction framework for Visium HD data: each WSI contains millions of��8μm��bins for the HD slide. A region-wise modeling strategy where each image region covers multiple bins is used to predict a high-resolution gene expression map, which enables more fine-grained and computationally efficient inference.

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