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MedTech giant Boston Scientific disclosed a cyber incident causing IT outages and disruption across its global operations.
Boston Scientific revealed that a cyber incident triggered IT outages disrupting its worldwide operations. The medical device manufacturer has not yet confirmed whether data was accessed or whether ransomware is involved. Details on scope and impact remain limited as the investigation continues.
Boston Scientific disclosed a cyberattack that caused a network outage, disrupting global operations including order processing and shipping.
The medical device maker detected the incident on August 25, activated incident response protocols with third-party cybersecurity experts, and told the SEC that access to systems supporting operations, including order processing and shipping, was disrupted. Boston Scientific employs about 59,000 people across 127 countries and posted more than $20 billion in net sales in 2025; it has not determined whether the incident is material and no group has claimed responsibility. The company joins a recent run of medtech attacks including Stryker, iRhythm, Novo Nordisk and Xsolis.
OpenAI announced ChatGPT integrations allowing healthcare organizations to connect EHR and industry data so clinicians can access patient context and research securely.
OpenAI said healthcare organizations can now connect electronic health records and other industry data sources to ChatGPT. The feature is aimed at letting clinicians securely access patient context and medical research within the assistant. The announcement was published on OpenAI's news site without disclosure of a new model release.
TeamPCP planted the SANDCLOCK credential stealer in LiteLLM PyPI releases, exposing credentials across 2,038 repositories at 898 organizations including Microsoft and NVIDIA.
Threat actor TeamPCP compromised LiteLLM maintainer credentials and published malicious versions 1.82.7 and 1.82.8 to PyPI around March 2026, creating an exposure window of several months. The SANDCLOCK credential stealer exposed full credential sets across 898 GitHub owners and 2,038 repositories, including Microsoft, Azure, IBM, NVIDIA, PayPal, Deloitte, Bosch, and S&P Global. Stolen material includes GitHub CI/CD identities, AWS/GCP/Firebase credentials, SSH keys, Kubernetes secrets, and OpenAI and Anthropic API keys; Resecurity acquired a 150GB archive with 2,146 credential records. Technology, banking/finance, and healthcare organizations are the most affected sectors, and victims must rotate all exposed credentials.
Boston Scientific says a cyberattack has caused global disruption to its operations, with no confirmation yet on device impact or data exfiltration.
Boston Scientific, a major medical device manufacturer, disclosed that a cyberattack is causing global disruption to its operations. The company has not confirmed whether medical devices are affected or whether any customer data was exfiltrated. The investigation appears to be ongoing, and healthcare-sector exposure raises patient-safety and supply-chain concerns.
ShinyHunters claims theft of 284 million patient records from McKesson via vishing, Okta takeover, and Salesforce/Snowflake access, demanding $55,236,150.
McKesson disclosed in an SEC filing a cybersecurity incident detected August 25, 2026, involving unauthorized access to third-party applications and data exfiltration affecting a subset of customers in its Oncology & Multispecialty and Medical-Surgical business units. ShinyHunters told BleepingComputer it entered through vishing calls to employees, used stolen credentials to take over Okta single sign-on accounts, and extracted about a terabyte of data from Salesforce and Snowflake environments over four days. The group claims 284 million database rows including names, addresses, Social Security numbers, Medicaid details, medical record numbers, and medication data, and demanded $55,236,150 with a 72-hour deadline; none of these claims have been independently verified.
Opinion piece argues AI could transform healthcare but warns the sector's surging cyber threats demand security-by-design and workforce awareness.
The commentary highlights AI's healthcare potential, citing Stanford's deep learning algorithm matching dermatologists at skin cancer detection and a UK review claiming full automation could save the NHS nearly £13bn annually. It notes healthcare experienced the largest year-over-year increase in cyber attacks, including targeting by state-sponsored groups such as Hidden Cobra. The piece urges organizations to build security in from the outset, hire cyber talent, address insider threats, and protect patient trust when rolling out AI.
Google's AMIE research medical AI system demonstrates real-time clinical video consultations in a first-of-its-kind simulated study.
Google introduced AMIE, its research medical AI system, demonstrating real-time clinical video consultation capabilities in a first-of-its-kind study. The evaluation was conducted in simulated settings, extending the AMIE diagnostic dialogue research line to multimodal video consultations. AMIE remains a research system rather than a deployed clinical product.
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.
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.
OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.
OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.
Google highlights AI-for-science advances: AlphaGenome Atlas mapping 9 billion genetic variants, WeatherNext 3 weather model, and global health AI tools.
Google detailed AI advances across science and health, including AlphaGenome Atlas, which mapped all 9 billion possible single-letter genetic changes in the human genome and was made openly available. WeatherNext 3 delivers 50% more accurate precipitation forecasts a day or more ahead and is already in products. AlphaFold is used by 4 million researchers in 190 countries, TB chest X-ray screening has processed 25,000+ scans across six nations, and the diabetic retinopathy model has supported 1.15 million screenings. Google also released its AI & Economy ATLAS global usage insights.
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.
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.
LongAgent autonomously searches variable sets and temporal windows to predict longitudinal medical outcomes, beating the strongest non-agent baseline on synthetic data.
The paper proposes LongAgent, an agent-based method that searches over combinations of variable sets, temporal windows and aggregation functions for outcome prediction on heterogeneous medical longitudinal data. It uses a history memory of previous searches and numerical evidence to guide exploration. On synthetic data it achieves mean RMSE 1.7376, improving over the best non-agent baseline by 0.0151 (95% CI [0.0045, 0.0260]; p=0.0273), and performs comparably to the best baseline on a real clinical dataset.
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.
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.
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.
OpenAI says Japanese company Polimill uses GPT models and Codex to give municipalities searchable access to administrative knowledge.
OpenAI published a customer story describing how Polimill, a Japanese civic technology company, builds public AI infrastructure using OpenAI GPT models and Codex. The tools let municipalities search and reuse administrative knowledge and are intended to accelerate internal development workflows.
DeepMind's AlphaGenome Atlas precomputes impact predictions for ~9 billion human DNA variants in a 1-petabyte dataset; its AVI score beats CADD in benchmarks
Google DeepMind released the AlphaGenome Atlas, precomputing functional-effect predictions for roughly 9 billion human genome variants (about 27,000 prediction values per variant) in a one-petabyte dataset more than 30 times the size of the AlphaFold database. The accompanying AlphaGenome Variant Impact Score (AVI), a small neural network combining AlphaGenome, AlphaMissense and evolutionary conservation features (18 inputs versus CADD's 150+), outperformed existing tools on clinically classified variants, ranking causal variants in the top 50 candidates for 29.5% of solved GREGoR cases versus 12.5% for CADD. A GREGoR epilepsy case illustrates the impact: AVI elevated a previously unclear DNM1 splice variant that lab experiments confirmed as likely disease-causing. The atlas is available for noncommercial use via web portal, API and a Google Antigravity skill, with a commercial version planned through Google Cloud.
Science article examines expert disagreement over whether a report on AI-enabled bioweapons risks is a chilling warning or an overreaction.
A Science.org article, shared on Hacker News with 20 points and 2 comments, covers expert divisions over an AI bioweapons report and whether its warnings are justified or exaggerated. The discussion reflects ongoing debate in the AI safety and biosecurity community about assessing AI's role in biological threat enhancement. Minimal detail is available from the item itself.
Vendors A10 Networks, ScienceLogic, Searchlight Cyber, and DataGrout/SelectHub released new security and AI operations products this week.
A weekly product roundup covering four releases: ScienceLogic Skylar AI 2.5 with expanded secure deployment options, Searchlight Cyber's Preemptive Threat Exposure Management platform combining exposure visibility with threat intelligence, the general availability of the A10 AI Gateway for routing and governing enterprise AI traffic, and DataGrout's LLM inference optimization and AI governance platform.
Google DeepMind launched AlphaGenome Atlas, precomputing molecular effect predictions and AVI impact scores for ~9 billion human single-nucleotide variants in a 1-petabyte catalogue.
Google DeepMind released AlphaGenome Atlas, a 1-petabyte catalogue of precomputed molecular effect predictions for roughly 9 billion possible single-nucleotide variants in the human genome. It introduces the AlphaGenome Variant Impact (AVI) score, combining AlphaGenome regulatory predictions with AlphaMissense, plus per-variant feature attributions and over 2,500 recurrent DNA sequence motifs. DeepMind reports best-in-class AVI performance on variant pathogenicity and rare disease benchmarks. Early users at the Broad Institute, University of Exeter, and Stowers Institute demonstrated rare-disease variant reprioritization and 22% more non-coding associations across 54,000+ UK Biobank genomes.
AI-powered baby monitors like Nanit, which raised $50 million, collect extensive infant data, raising privacy and surveillance concerns.
A New York Times report covered by Schneier on Security describes how baby-monitoring companies such as Nanit are expanding AI-based 24/7 health tracking of infants. Nanit recently raised $50 million to add tracking of speech and language development and motor skills via its camera, while extending its bedroom presence into early adolescence. The post highlights the already extensive level of baby data collection and its expected growth.
Proposes Discovery Foundation Models that participate in creating new problems and knowledge, instantiated in Zetema and the GALILEO therapeutic-discovery system.
The paper formulates Discovery Foundation Models (DFMs) as general-purpose systems for open-ended discovery, supporting seven coupled capabilities from problem discovery through evidence-grounded revision and continual improvement. It instantiates the framework with Zetema, combining explicit research-state dynamics, verification gating, external grounding, and cross-task Discovery Skill evolution. GALILEO, a real therapeutic-discovery system, closes the loop between Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, and iterative hypothesis revision. The authors also define process-centered evaluation so discovery behavior can be trained and measured beyond final answers.
Google publishes a collection page showcasing partnerships applying AI to health, disaster prediction, learning, and economic opportunity.
Google's 'AI for Societal Impact' page curates examples of experts and local leaders applying AI to disease detection and prevention, natural disaster prediction, education, and economic opportunity. It is a marketing-oriented collection page with no new technical details.
Researchers release Dr. Claw, an open-source auditable workspace that wraps coding agents like Claude Code for end-to-end AI-assisted research workflows.
Paper 2609.00365 presents Dr. Claw, an open-source workspace that wraps existing coding-agent executors such as Claude Code and Gemini CLI in a controllable, human-in-the-loop research workflow. It uses persistent state objects, a reusable skill library, and multi-executor coordination to make research decisions auditable and recoverable, rather than adding another autonomous agent. Holding the executor fixed, Dr. Claw scores higher on research completeness than a bare command-line agent while preserving an auditable process trail. The code is released under AGPL-3.0 on GitHub (OpenLAIR/dr-claw).
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.
OpenAI launched GPT-6 Astra, its most capable and aligned model, in ChatGPT Work, Codex, and the API, claiming frontier performance and cybersecurity gains.
GPT-6 Astra is now available in ChatGPT Work, Codex, and the API, with OpenAI claiming state-of-the-art performance in computer use, browsing, professional work, software engineering, cybersecurity, and science. Astra is billed as the first model to reach the Critical cybersecurity capability threshold under OpenAI's Preparedness Framework, with 89% fewer unintended outcomes than GPT-5.6 Sol and 74.7% fewer than Claude Fable 5.1 on OpenAI's internal computer-use safety benchmark. Pricing starts at $10 per million input tokens and $50 per million output tokens, with claims of occupying most of the cost-efficiency frontier on Terminal Bench 4.0 and the Artificial Analysis Intelligence Index. New enterprise admin controls and plugins from Oracle Analytics, Power BI, Navan, and Avalara launched alongside the model.
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.
A Biofile report based on IBM X-Force data finds 60% of cyberattacks in Colombia target healthcare institutions, making medical data a prime target.
A Biofile report drawing on IBM's X-Force Index found that six out of ten cyberattacks recorded in Colombia target health sector institutions. The finding highlights the growing exposure of hospitals and clinics to digital threats. Medical information has become one of the main targets for attackers in the country.