Science-driven
clinical AI.

We present a multimodal transformer architecture that integrates streaming vital signs, laboratory results, clinical notes, and medication administration data to predict sepsis onset 48-72 hours before clinical presentation. In a prospective validation across 6 academic medical centers (n=42,318 ICU admissions), the model achieved an AUROC of 0.943 (95% CI: 0.938-0.948) with a sensitivity of 94.1% at a specificity of 89.3%, significantly outperforming existing early warning scores including qSOFA (AUROC 0.72) and MEWS (AUROC 0.68).

Alert fatigue remains a critical patient safety concern in hospital settings. We conducted a cluster-randomized trial across 14 ICUs in 4 health systems (n=8,724 patients) to evaluate an AI-driven contextual alarm filtering system. The intervention group demonstrated a 68% reduction in non-actionable alarms (p<0.001) while maintaining a 99.7% sensitivity for clinically significant events. Nursing satisfaction scores improved by 34 points and response time to genuine critical alerts decreased by 22%.

Standard formulary-based drug interaction checking fails to account for patient-specific pharmacogenomic variations, renal function, and hepatic metabolism. In this 12-month evaluation across 3 health systems (n=156,832 medication orders), our pharmacogenomic-enhanced system detected 3.2 times more clinically significant interactions than the standard system (p<0.001), including 847 interactions classified as potentially life-threatening that would have been missed entirely by conventional checking.

Training clinical AI models typically requires centralizing patient data, creating privacy and governance challenges. We developed a federated learning framework that enables model training across 8 partner institutions without sharing raw patient data. The federated model achieved 96.8% of the performance of a centrally-trained model (AUROC 0.937 vs. 0.968) while maintaining full HIPAA compliance and institutional data sovereignty. Differential privacy guarantees were validated through formal analysis.

Diagnostic error in the emergency department contributes to significant morbidity and mortality. This stepped-wedge cluster trial across 8 emergency departments (n=31,562 encounters) demonstrated that AI-assisted clinical decision support reduced the diagnostic error rate from 5.7% to 2.1% (relative reduction 63.2%, p<0.001). Time to correct diagnosis decreased by 41%, and 30-day unplanned return visits decreased by 28%. Clinician acceptance of AI recommendations was 89%.

We introduce a temporal convolutional network architecture optimized for real-time patient deterioration detection from streaming physiological data. The architecture processes up to 2,000 data points per second per patient with sub-50ms inference latency on standard GPU hardware. Evaluated on the MIMIC-IV dataset, the model achieved state-of-the-art performance on cardiac arrest prediction (AUROC 0.961), respiratory failure (AUROC 0.948), and acute kidney injury (AUROC 0.923).

Ensuring equitable performance of clinical AI across demographic groups is an ethical imperative. We present a comprehensive bias auditing framework tested across 14 million de-identified patient records from 8 health systems. Initial model performance disparities of up to 4.2% across racial and ethnic groups were reduced to below 0.8% through targeted data augmentation, fairness-aware loss functions, and continuous monitoring. The framework is publicly available for adoption by other clinical AI developers.

Acute kidney injury (AKI) following major surgery affects up to 30% of ICU patients and is associated with significantly increased mortality. We developed a temporal deep learning model that predicts AKI 24-48 hours before serum creatinine elevation, using streaming data from 22,847 surgical ICU admissions. The model achieved an AUROC of 0.923 with 87.4% sensitivity at 90.1% specificity, enabling early intervention that reduced AKI progression to Stage 3 by 31% in a subsequent pilot study.

Clinical AI systems must provide transparent reasoning to earn clinician trust and satisfy regulatory requirements. We developed an optimized SHAP-based feature attribution pipeline that generates real-time explanations for diagnostic predictions without significantly impacting inference latency (additional 12ms overhead). A user study with 124 physicians found that transparent AI explanations increased diagnostic confidence by 27% and willingness to adopt AI-assisted tools by 42%.

Integrating AI tools into existing electronic health record workflows remains a significant barrier to clinical AI adoption. Drawing from our experience deploying clinical AI across 40+ health systems using HL7 FHIR R4, we document key architectural patterns, common integration challenges, and performance optimization strategies. Our FHIR-native approach reduced integration timelines from an industry average of 18 months to 6-8 weeks while achieving 99.97% data fidelity.

Hospital-acquired infections (HAIs) affect approximately 1 in 31 hospital patients and carry significant morbidity, mortality, and cost. In this prospective cohort study across 11 hospitals in 3 countries (n=94,216 admissions), our AI system detected HAIs a median of 36 hours earlier than clinical identification (IQR: 18-52 hours). Early detection was associated with 23% shorter infection duration, 18% lower antibiotic utilization, and an estimated cost saving of $4,200 per detected case.