ARMD‑Predict is a multi‑site machine‑learning system trained on 7.5 million culture‑antibiotic records from four academic medical centers to forecast antibiotic resistance before culture results are available. It offers two tiers of models: one set predicts susceptibility to individual antibiotics, while a hierarchical set first identifies the infecting organism and then its resistance profile. In internal testing, the models achieved median AUROCs of 0.77 for inpatient urine and 0.70 for inpatient blood cultures, and could flag a substantial proportion of ceftriaxone‑susceptible and resistant cases for narrower or broader therapy, respectively.
By Fateme Nateghi Haredasht, Nicholas P. Marshall, Wenyuan Chen, Lauren N. Cooper, Ziming Wei, Harrison Konsker, Jacob S. Chen, Michael Klompas, Cara McKenna, Richard J. Medford, Sanjat Kanjilal, Austin J. Schoeffler, Stephen P. Ma, Niaz Banaei, Steven M. Asch, Amy Chang, Stanley C. Deresinski, Mary K. Goldstein, Robert Tibshirani, Jonathan H. Chen
The study presents an open‑source multimodal model that classifies bacterial versus fungal microbial keratitis using 17,158 slit‑lamp images from 1,645 patients in the US and India. The model, which integrates cross‑modality contrastive learning, modality‑specific fine‑tuning, and seven clinical variables, achieved a macro‑F1 of 83.06% and an AUROC of 89.9% overall, outperforming five comparator methods. However, performance varied markedly by country—AUROC 83.9% in India versus 67.0% in the US—highlighting geographic confounding and the need for independent geographic validation.
By Yiqing Wang, Maria A. Woodward, Ziyun Yang, N. Venkatesh Prajna, Chunming He, Leslie M. Niziol, Mercy Pawar, Ming-Chen Lu, Guillermo Amescua, Rachel A. F. Wozniak, Sejal Amin, Abinaya Krishnan, Prabhleen Kochar, Sina Farsiu
arXiv:2609.21332v1 Announce Type: new
Abstract: Acute infectious diseases are among the leading causes of medical consultations and hospitalizations in children worldwide. These infections are predom...
By Mihaela Demireva, Zhecho Mitev, Djuna Chinareva-Klimentova, Svetoslav Ivanov, Georgi Nalbantov, Dimitar Mitev
arXiv:2605.22611v2 Announce Type: replace
Abstract: Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum ant...
By Niklas Raehse, Luregn J. Schlapbach, Daphn\'e Chopard
arXiv:2602. 22673v2 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat.
By Md Tanvir Hasan Turja
arXiv:2607. 14070v1 Announce Type: cross Abstract: Genomic foundation models such as Evo 2 learn rich sequence representations, but their value for biosecurity screening is largely unexplored.
By Jeremy Guntoro, Alexander Dack, Dylan Danno, Michaela Jan\v{c}ovi\v{c}ov\'a, Kri\v{z}an Jurinovi\'c, Vanessa Smilansky
DRYLAB‑Bench is a new benchmark that measures the biosecurity risk of large language models (LLMs) when they design biological mutations. It presents 30 hazardous mutation‑design scenarios and evaluates LLMs in a multi‑round, tool‑augmented setting, scoring designs against 187,695 empirical wet‑lab measurements using a Biosecurity Risk Score. Initial tests on 15 frontier models show that even without tools, a majority of designs exceed average risk, and tool use further amplifies the threat.
By Yuzhen Wang, Chunxiao Li, Baichen Le, Lewen Yan, Jie Zhang, Yucheng T. Yang, Xing-Ming Zhao, Lijun Li, Jing Shao
The study evaluates the robustness of medical vision‑language models for tuberculosis screening on chest X‑rays by testing them across multiple datasets, prompts, and evaluation settings. Three specialized models (BioMedCLIP, CheXficient, MedSigLIP) and a general OpenCLIP model were audited on 12,200 images, producing 244,000 model–image–prompt scores. Results show that no model consistently outperforms others across all cohorts and reliability criteria, with prompt changes and control group composition significantly affecting AUROC, and that high training‑set performance does not reliably transfer to external cohorts.
By Mushir Akhtar, M. Tanveer, Mohd. Arshad
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
By Jessica Sena, Shesadree Priyadarshani, Miguel Contreras, Bharat Gandhi, Scott Siegel, Subhash Nerella, Parisa Rashidi
arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.
By Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari, Tahir Qasim Syed
arXiv:2608. 05244v1 Announce Type: cross Abstract: We developed a unified covariate-adjusted causal inference framework for estimating the desirability of outcome ranking (DOOR) probability for benefit-risk evaluation in randomized trials and observational studies.
By Yuan Feng, Shiyu Shu, Yixin Fang, Ionut Bebu, Toshimitsu Hamasaki, Scott Evans, Guoqing Diao
arXiv:2608. 07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance.
By Xiaohua Douglas Zhang