SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
Accuracy and efficiency of drilling trajectories with augmented reality versus conventional navigation randomized crossover trial
9 Pith papers cite this work. Polarity classification is still indexing.
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VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
Echo-POSED learns an SO(3)×SO(3) pose representation via self-supervised equivariance to probe motion and invariance to cardiac phase from 2D slices of 3D echocardiography volumes, reporting 8.2° mean angular error in intra-patient guidance simulations.
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
Physician oversight reveals high error rates in LLM-generated labels for a clinical benchmark and demonstrates that corrected labels improve both evaluation accuracy and downstream model training.
User study with 30 novices establishes performance baselines for freehand 5D AR trajectory following and shows orientation constraints create cognitive-motor trade-offs that some visual UIs can mitigate.
Unsupervised discriminator-guided fine-tuning of a pretrained u-sleep model improves Cohen's kappa by 0.03-0.29 on artificially degraded sleep signals but falls short of supervised optima and yields insignificant gains on real domain mismatches.
Transfer learning from an arachnophobia dataset yields 86% accuracy classifying PTSD status and 17% MAPE estimating severity via HR/GSR signals and MKDE in a 21-person military cohort.
A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.
citing papers explorer
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Scalable Stewardship of an LLM-Assisted Clinical Benchmark with Physician Oversight
Physician oversight reveals high error rates in LLM-generated labels for a clinical benchmark and demonstrates that corrected labels improve both evaluation accuracy and downstream model training.
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End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians
A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.