Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Did a Chatbot Write a Prize-Winning Story? If the possibility that one or more of the winners of the Commonwealth Short Story Prize was A.I.-generated chills us, it may be because of what it reveals about human writing. In early May, the Commonwealth Foundation announced the five regional winners for its influential Short Story Prize, which recognizes unpublished short fiction. One of the awardees, a Trinidadian writer named Jamir Nazir, was accused of A.I.-assisted cheating by a broad array of social-media users who seized upon his story's synthetic tics, glitchy metaphors, and general unreadability. "Maybe it was a name; maybe rain took a shape and decided to keep it.")

Machine learning-based decision support systems are increasingly deployed in clinical settings, where probabilistic scoring functions are used to inform and prioritize patient management decisions. However, widely used scoring rules, such as accuracy and AUC-ROC, fail to adequately reflect key clinical priorities, including calibration, robustness to distributional shifts, and sensitivity to asymmetric error costs. In this work, we propose a principled yet practical evaluation framework for selecting calibrated thresholded classifiers that explicitly accounts for uncertainty in class prevalences and domain-specific cost asymmetries. Building on the theory of proper scoring rules, particularly the Schervish representation, we derive an adjusted variant of cross-entropy (log score) that averages cost-weighted performance over clinically relevant ranges of class balance. The resulting evaluation is simple to apply, sensitive to clinical deployment conditions, and designed to prioritize models that are both calibrated and robust to real-world variations.

Pathology foundation models (PFMs) have emerged as powerful tools for analyzing whole slide images (WSIs). However, adapting these pretrained PFMs for specific clinical tasks presents considerable challenges, primarily due to the availability of only weak (WSI-level) labels for gigapixel images, necessitating multiple instance learning (MIL) paradigm for effective WSI analysis. This paper proposes a novel approach for single-GPU \textbf{T}ask \textbf{A}daptation of \textbf{PFM}s (TAPFM) that uses vision transformer (\vit) attention for MIL aggregation while optimizing both for feature representations and attention weights. The proposed approach maintains separate computational graphs for MIL aggregator and the PFM to create stable training dynamics that align with downstream task objectives during end-to-end adaptation. Evaluated on mutation prediction tasks for bladder cancer and lung adenocarcinoma across institutional and The Cancer Genome Atlas (TCGA) cohorts, TAPFM consistently outperforms conventional approaches, with H-Optimus-0 (TAPFM) outperforming the benchmarks. TAPFM effectively handles multi-label classification of actionable mutations as well. Thus, TAPFM makes adaptation of powerful pre-trained PFMs practical on standard hardware for various clinical applications.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

A medical student asks questions to surgical support software tool that uses artificial intelligence. Moves are underway in Japan to develop artificial intelligence tools designed to help reduce burdens on surgeons. While the number of cancer patients in the country is projected to peak in around 2040 amid an aging population, too few people want to become surgeons due to the job's challenging work environment. The government is responding to the crisis by supporting companies working to develop AI technology to help surgeons. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Foundation models trained on large-scale de-identified electronic health records (EHRs) hold promise for clinical applications. However, their capacity to memorize patient information raises important privacy concerns. In this work, we introduce a suite of black-box evaluation tests to assess privacy-related memorization risks in foundation models trained on structured EHR data. Our framework includes methods for probing memorization at both the embedding and generative levels, and aims to distinguish between model generalization and harmful memorization in clinically relevant settings. We contextualize memorization in terms of its potential to compromise patient privacy, particularly for vulnerable subgroups. We validate our approach on a publicly available EHR foundation model and release an open-source toolkit to facilitate reproducible and collaborative privacy assessments in healthcare AI.

Estimating treatment effects is crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient information. However, at inference time, predictions are often made using textual descriptions (e.g., descriptions with self-reported symptoms), which are incomplete representations of the original patient information. In this work, we make three contributions.

When you purchase through links in our articles, we may earn a small commission. Get officially certified in Claude AI for just $19.99 A Claude AI Professional E-Degree is on sale for $19.99 (reg. AI skills are no longer a nice-to-have. A verifiable credential in one of the most popular AI models on the market is a real resume differentiator, and right now, you can get an e-degree in Claude for just $19.99 (reg. While plenty of people have dabbled with Claude, there's a big difference between "I've used it a few times" and actually knowing how to make it work for you.