ehr
15 articles about ehr in AI news
ChatHealthAI: EHR Foundation Model + Frozen LLM Hits 79.8% F1 on Length-of-Stay
ChatHealthAI aligns CLMBR-T-Base with a frozen LLM via a task-aware resampler, achieving 79.8% F1 on EHRSHOT length-of-stay prediction while enabling interpretable reasoning.
Aehr Test Systems Lands $41M AI Chip Order; H2 Bookings Top $92M
Aehr Test Systems received a record $41 million production order from a key hyperscale AI customer. Total bookings for the second half of its fiscal year exceeded $92 million, highlighting surging demand for semiconductor test and burn-in equipment.
LLM Schema-Adaptive Method Enables Zero-Shot EHR Transfer
Researchers propose Schema-Adaptive Tabular Representation Learning, an LLM-driven method that transforms structured variables into semantic statements. It enables zero-shot alignment across unseen EHR schemas and outperforms clinical baselines, including neurologists, on dementia diagnosis tasks.
Vermont Blocks AI Data Center Bill as Infrastructure War Intensifies
Vermont blocked a bill regulating AI data centers, rejecting the first U.S. state-level attempt to govern AI infrastructure. The vote signals growing tension between buildout and local regulation.
Apple Paper Argues LLMs Show 'Illusion of Thinking'
Apple paper argues LLMs show no genuine reasoning, only pattern matching. The critique targets vendor claims but lacks new empirical evidence.
NATO Tests SWARM Biotactics' AI-Guided Cyborg Cockroaches for Recon
NATO is evaluating a biohybrid system from German defense startup SWARM Biotactics, which uses AI to guide live cockroaches fitted with sensor backpacks through complex environments for military reconnaissance.
llm-anthropic 0.25 Adds Opus 4.7 with xhigh Thinking Effort — Here's How
Update to llm-anthropic 0.25 to access Claude Opus 4.7 with xhigh thinking_effort for tackling your most challenging code problems.
Google's Memory Caching Bridges RNN-Transformer Gap with O(NL) Complexity
Google's 'Memory Caching' method saves RNN memory states at segment boundaries, allowing tokens to reference past checkpoints. This O(NL) approach significantly improves RNN performance on recall tasks, narrowing the gap with Transformers.
How a Healthcare Startup Used Claude Code to Ship 66 Architecture Tickets in 4 Hours
Claude Code can autonomously execute complex architecture work when given proper domain expertise, ticket planning, and execution authority—no magic required.
Legion Health AI Approved for Psychiatric Prescription Renewals in California
San Francisco startup Legion Health received regulatory approval for its AI system to autonomously renew a narrow set of psychiatric prescriptions for stable patients. This represents a carefully guardrailed but significant step toward AI-assisted clinical workflow.
DISCO-TAB: Hierarchical RL Framework Boosts Clinical Data Synthesis by 38.2%, Achieves JSD < 0.01
Researchers propose DISCO-TAB, a reinforcement learning framework that guides a fine-tuned LLM with multi-granular feedback to generate synthetic clinical data. It improves downstream classifier utility by up to 38.2% versus GAN/diffusion baselines and achieves near-perfect statistical fidelity (JSD < 0.01).
STAR-Set Transformer: AI Finally Makes Sense of Messy Medical Data
Researchers have developed a new transformer architecture that handles irregular, asynchronous medical time series by incorporating temporal and variable-type attention biases, outperforming existing methods on ICU prediction tasks while providing interpretable insights.
MIRAGE AI Framework Bridges Critical Gap in Alzheimer's Diagnosis by Synthesizing MRI Insights from Health Records
Researchers have developed MIRAGE, a novel AI framework that uses knowledge graphs to synthesize diagnostic MRI information from electronic health records, potentially revolutionizing Alzheimer's disease assessment in resource-limited settings by bridging the missing-modality gap.
Beyond the Hype: New Benchmark Reveals When AI Truly Benefits from Combining Medical Data
A comprehensive new study systematically benchmarks multimodal AI fusion of Electronic Health Records and chest X-rays, revealing precisely when combining data types improves clinical predictions and when it fails. The research provides crucial guidance for developing effective and reliable AI systems for healthcare deployment.
AI Deciphers Patient Language to Predict Stroke Risk with Unprecedented Precision
Researchers have developed an AI system that analyzes patient-reported symptoms to detect early stroke risk in diabetic individuals. Using graph neural networks and patient-centered language, the system achieves near-perfect predictive accuracy while minimizing false alarms.