Search: “Premier Medical Group”

280,000 Impacted by Premier Medical Group Data Breach

New York healthcare provider Premier Medical Group is notifying 282,075 patients that personal and medical information was stolen in a June breach.

New York healthcare provider Premier Medical Group is notifying 282,075 patients whose personal and medical data was stolen in a June breach. Attackers accessed certain files on June 14 after some systems were disrupted, but PMG has not disclosed how the attack occurred or who was responsible. Exposed data includes names, contact information, dates of birth, treatment and diagnostic details, medication information, and health insurance details. PMG reported the incident to HHS, which added it to its public breach portal this week.

SecurityWeek·Sep 16, 10:47 UTC · 17h agoData breach in the wild

Virtual Event Today: Attack Surface Management Summit

SecurityWeek's 2026 Attack Surface Management Summit runs today as a virtual event covering asset discovery, SBOMs, red teaming, and pen-testing.

SecurityWeek is hosting its fully virtual 2026 Attack Surface Management Summit from 11AM-3PM, focused on continuous asset discovery, prioritization, and risk reduction. Sessions cover proving exploitability, SBOM and AIBOM software supply chain risk with Dr. Allan Friedman, demos from Wiz and Horizon3's NodeZero, and the roles of red teaming, bug bounty, and penetration testing in enterprise defense.

SecurityWeek·Sep 16, 14:35 UTC · 13h agoIndustry

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

A retrospective study found GPT-4 over-flagged emergency department revisit cases while an LLM knowledge-graph screener achieved 83-100% positive predictive value.

In an exploratory retrospective study of 99 emergency department diagnosis pairs from a multihospital health system, clinicians and GPT-4 independently judged whether revisit pairs warranted further assessment. GPT-4 responses correlated poorly with clinicians, flagging 94% of pairs for follow-up, 4.4-13.3 times more than clinicians, though prompt engineering was minimal. An algorithm leveraging an LLM-populated knowledge graph (KGA) achieved 83-100% positive predictive value against at least one clinician rater, suggesting LLM-based screening could broaden revisit quality review without substantially increasing reviewer workload.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:37 UTC · 7d agoAI research1

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed-FL benchmarks multimodal federated learning for chest radiograph diagnosis across 3-20 clients, with FedProx leading under severe non-IID skew.

OmniMed-FL studies multimodal federated learning combining chest radiographs and clinical notes for five-class condition classification under HIPAA/GDDR-compliant decentralized training. It benchmarks eight fusion strategies, imputation rules, and federated baselines under Dirichlet non-IID partitioning across 3-20 hospital clients. With 5 clients and severe skew (alpha=0.1), FedProx scored 0.737 macro-F1 versus 0.662 for FedAvg and 0.297 for local-only training. Multimodal fusion beat unimodal inputs (0.956 vs 0.934 text, 0.664 images) on the synthetic corpus.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 15:56 UTC · 7d agoAI research1

Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics

Clinical AI system Doctorina achieved 82.0% primary-care diagnostic concordance versus 57.0% for physicians across 150 synthetic consultations.

The study compared Doctorina, eight physicians, and four standalone frontier language models on 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 diagnostic concordance versus 57.0% for physicians (25.0-point difference, 95% CI 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Kimi K3 ranked next on diagnosis, while Claude Opus 5 led the closely spaced management estimates among Opus, Doctorina and Kimi.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:25 UTC · 8d agoAI research

Brinqa acquires PlexTrac to bring validated remediation to exposure management

Brinqa acquired PlexTrac to add pentest-driven validation of remediation, claiming the largest standalone unified exposure management vendor with 3,000+ customers.

Brinqa acquired PlexTrac to add offensive-security-driven validation that remediation actually worked, aiming to close the continuous threat exposure management loop from prioritization through verified fixes. Brinqa says the combination makes it the largest standalone vendor in unified exposure management, serving more than 3,000 customers across 57 countries, with PlexTrac continuing as a standalone product. Brinqa cited 164% year-over-year new-bookings growth in 2025 and inclusion in Gartner's inaugural Magic Quadrant for Exposure Assessment Platforms.

Help Net Security·Aug 19, 00:00 UTC · 29d agoIndustry

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

The Model Proposes, the Code Disposes: A Pre-Registered Ablation of a Verifier-and-Acceptance Stage in an LLM-Orchestrated Offensive-Security Agent

Pre-registered ablation finds a model verifier stage in an LLM offensive-security agent suppresses findings; removing it eliminated suppression with precision tradeoff.

The paper evaluates a verifier-and-acceptance stage in an LLM-orchestrated offensive-security agent via a pre-registered 20-run confirmatory ablation and a 2x2 factorial study with 40 runs on vulnerable lab targets. Removing the stage eliminated pre-report suppression (median 2 vs 0 findings, p = 0.00003) but reduced model-blinded shipped precision (0.471 vs 0.353, p = 0.0087). Suppression was attributed to the model verifier rather than deterministic acceptance rules, and an instrumented canary recorded zero external contacts in all 60 runs. The full design retained 93.8% of model-adjudicated true candidates but failed its pre-registered non-inferiority floor of 0.90.

arXiv cs.CR·Sep 14, 17:13 UTC · 2d agoResearch

Brown Health Medical Group-MA Data Breach Exposes Information of 311,000 Individuals

Security Affairs·Aug 5, 16:27 UTC · Aug 5, 2026Data breach

Nutex Health Says Patient Data Stolen, Hackers Threaten Leak

The Gentlemen ransomware gang claims breach of US healthcare provider Nutex Health, exfiltrating patient and employee data and threatening publication.

Nutex Health disclosed in an SEC 8-K filing that an unauthorized third party accessed and exfiltrated patient, employee, credentialed provider, business, and financial data from company servers, and threatened to publish it. The Gentlemen ransomware group listed Nutex on its leak site; a class action was filed August 27 and Edelson Lechtzin LLP is separately investigating. Nutex operates over 27 facilities in 12 states and served nearly 100,000 patients in the first half of 2026, with no material operational impact identified so far.

Infosecurity Magazine·Sep 2, 10:45 UTC · 14d agoRansomware

Healthcare and Victim Support Charities Affected by Beacon Cyber Incid

Infosecurity Magazine·Aug 7, 10:45 UTC · Aug 7, 2026Data breach

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:49 UTC · 2d agoAI safety & security

Microsoft named a Leader in the Frost Radar™: Cloud Workload Protection Platforms, 2026

Microsoft was named a leader in the 2026 Frost Radar for Cloud Workload Protection Platforms, cited for Defender for Cloud runtime security.

The 2026 Frost Radar for Cloud Workload Protection Platforms placed Microsoft in its leadership tier, calling the company a visionary leader. Microsoft attributes the recognition to unified runtime security delivered through Microsoft Defender for Cloud. The post is vendor marketing with no incident, vulnerability, or technical detail.

Microsoft Security Blog·Aug 19, 17:30 UTC · 28d agoIndustry3

Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Audits of 10 classifiers on BRFSS show target leakage, not model class, drives the reported 0.89 AUROC in survey-based cardiovascular screening.

The study benchmarks ten model classes, including glass-box and tabular foundation models, for prevalent myocardial infarction on 442,067 respondents of the 2022 BRFSS across five feature tiers of decreasing leakage risk. Removing two post-diagnostic features costs every model 0.049-0.051 AUROC and collapses performance into a 0.0045-wide band, and the explainable boosting machine matches all alternatives within 0.005 while scoring roughly 104x faster than the strongest foundation model. Frozen models transport within 0.002 AUROC to 2023 data; the authors conclude evaluation practice and feature sets, not model capacity, are the binding constraint.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:29 UTC · 6d agoAI research1

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

Hugging Face guide fine-tunes a 350M-parameter model with 100 GRPO steps to improve structured output reliability.

A Hugging Face blog post demonstrates fine-tuning a 350M-parameter model using GRPO (Group Relative Policy Optimization) with TRL over 100 training steps. The stated goal is more reliable structured outputs from small language models. No article body was available, so details beyond the title are limited.

Hugging Face Blog·Sep 3, 00:00 UTC · 14d agoAI tools & infra

Unlimited Technology Systems Data Breach Exposes Data of 3.8 Million Healthcare Patients

Security Affairs·Aug 8, 07:25 UTC · Aug 8, 2026Data breach

ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs

ModaLens image-swap audit shows report availability cuts MedGemma-27B image sensitivity on MIMIC-CXR from 20.94% to 4.26% answer changes.

ModaLens is a paired image-swap audit measuring how report availability affects image sensitivity in report-conditioned medical VLMs. On MedGemma-27B across 3,199 paired MIMIC-CXR cases from 293 patients (14 questions per case), generated answers changed on 4.26% of image-swap trials with the report versus 20.94% without it, a 16.7-point paired difference (95% CI 15.6-17.7). The original prompt with a lowercase first-token readout gave 4.70% versus 17.07%, and the direction replicated in two further model lineages. Labels derived from reports limit conclusions about visual correctness; code, prompts, and run records are publicly released.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research

Healthcare cyberattacks hit pacemakers and millions of patient records

McKesson confirms a data breach as ShinyHunters demands $55.2M amid healthcare cyberattacks affecting millions of patient records and pacemaker devices.

McKesson, one of the largest US healthcare distribution companies, has admitted a data breach. The ShinyHunters group is demanding a $55.2M extortion payment. The Register reports broader healthcare cyberattacks tied to the story, affecting millions of patient records and involving medical devices such as pacemakers. Details on the exact number of compromised records have not been confirmed.

The Register · Security·Aug 31, 22:10 UTC · 16d agoData breach in the wild

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:44 UTC · 2d agoAI research1

Dental contractor set up secret account with access to 4,000 patient records then left the company

A dental practice left an unknown vendor admin account with access to 4,000 patient records active for over three years, creating HIPAA risk.

Chris Kirksey, CEO of Direction, found three admin accounts on a dental practice's patient database, including one belonging to a scheduling company dropped in 2021 that retained access to 4,000 patient records for at least three years. A contractor had created the account without telling anyone and then left, so nobody knew to remove it, creating HIPAA compliance risk. Kirksey removed the accounts, adopted mandatory vendor access shutdown and twice-yearly reviews, and later found similar orphaned-account issues at six other healthcare practices.

The Register · Security·Sep 10, 07:00 UTC · 6d agoIndustry

Prompt Injection Remains Biggest LLM Risk, Despite Limited Incidents

Infosecurity Magazine·Aug 5, 11:00 UTC · Aug 5, 2026AI safety & security

Help shape the future of resilient private 5G

The UK NCSC invites organizations to collaborate on developing secure, resilient and deployable private 5G network technologies.

The UK National Cyber Security Centre is seeking collaboration with organizations developing technologies and approaches for secure, resilient and deployable private 5G networks. The blog post is an open call to help shape future private 5G resilience.

NCSC UK·Aug 12, 12:00 UTC · Aug 12, 2026Advisory

Change Healthcare Cyber

Change Healthcare's ransomware attack disrupted US healthcare payments and exposed data of roughly 190 million people.

Infosecurity Magazine covers the cyberattack on Change Healthcare, the UnitedHealth Group subsidiary hit by ALPHV/BlackCat ransomware. The attack disrupted US pharmacy and payment processing services and led to the exposure of data belonging to about 190 million individuals. It ranks among the largest healthcare sector cyber incidents on record.

Infosecurity Magazine·Aug 19, 00:00 UTC · 29d agoRansomware in the wild

WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

WearableQA benchmark introduces 4,084 questions over real longitudinal wearable data, showing 14 LLMs score 19.6-72.9% on health reasoning, far from solved.

WearableQA is a benchmark of 4,084 10-option multiple-choice questions built from wearable time series, blood biomarkers, and demographics of 200 real users with up to 500 days of daily measurements. It defines 16 question types along two axes: data versus health reasoning, and single- versus cross-signal reasoning, using a dual-grounding framework combining literature and population-validated patterns. Evaluations of 14 proprietary and open-source LLMs show accuracy ranging from 19.6% to 72.9% against a 10% chance baseline, with most models below 60%.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

WarmBloodAban/Minimax-h3_Singularity — new model trending #22 on Hugging Face

Community fine-tune Minimax-h3_Singularity enhances MiniMax-H3 video generation with HDR quality, distant face restoration, and improved motion, trending #22 on Hugging Face.

Minimax-h3_Singularity is a community fusion fine-tune of the MiniMax-H3 multimodal video generation model, built from multiple checkpoints and refined with pruning and weight optimization. It supports Text-to-Video, Image-to-Video, Reference-to-Video, and Video-to-Video workflows in ComfyUI, and claims improvements in HDR clarity, distant face restoration, motion fluidity, and fantasy VFX. The authors recommend pairing it with the minimax_h3_ref2v_turbo_4step_v0.1 LoRA for four-step accelerated inference, and an online demo is available via RunningHub.

Hugging Face trending models·Sep 5, 18:51 UTC · 11d agoModel release7· 1 read

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:56 UTC · 8d agoAI research2

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a time-aware generative transformer trained on 559 million MIMIC clinical events to forecast multimodal patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer trained on over 559 million clinical events from 431,000 hospital visits by 299,000 patients across the MIMIC dataset family. It uses bidirectional time integration and a variational latent space to model the stochastic evolution of patient states, natively processing medical images, time-series signals, categorical events, and structured or unstructured clinical records. The model supports autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation, with strong probing performance across clinical outcomes, 15 ICD chapters, and 29 comorbidities.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research1