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
7 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
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.
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.
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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SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
SGC-RML creates an 8D symptom atlas from multimodal PD data and integrates conformal calibration to deliver reliable, rejectable longitudinal assessments.
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VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
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.
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Fairboard: a quantitative framework for equity assessment of healthcare models
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.
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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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Hot Wire 5D+: Evaluating Cognitive and Motor Trade-offs of Visual Feedback for 5D Augmented Reality Trajectories
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.
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Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism
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.
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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.