The decentralized OASIS study analyzed smartwatch biometric data and patient-reported outcomes from 532 SLE participants. A 94-feature regression model achieved significant correlation (R²=0.75) predicting self-reported flares, supporting proactive clinical screening.
Read MoreThis study combined natural language processing of medical records with patient biometric and self-reported data. Strong correlations identified 24 metrics predicting physician assessments, supporting AI-augmented remote lupus management strategies.
Read MoreThis prospective study evaluated aiSLE® MGMT engagement platform in SLE patients across five US practices. Participants showed improved quality-of-life metrics, reduced fatigue, and decreased physician-reported disease activity, demonstrating comprehensive care management benefits.
Read MoreThis study used a virtual/digital program to recruit at-risk individuals via online screening and sequential telehealth evaluations, classifying 18% with SLE in a mean of 371 days—shortening typical diagnosis time from 5-7 years—demonstrating potential for remote, accurate lupus assessment.
Read MoreThis decentralized study used machine learning to assess patient-reported outcomes, quality-of-life measures, and smartwatch biometric data from SLE patients. Models achieved significant predictive accuracy for disease flares, supporting proactive clinical screening.
Read MoreThis pilot study evaluates a comprehensive SLE management platform combining a Lupus Flare Risk Index biomarker, smartwatch-interfaced mobile app, and health coaching to improve patient self-efficacy and disease outcomes.
Read MoreThis study evaluated generative AI using ACR 1997 criteria to predict systemic lupus erythematosus classification from medical records. Results showed 76% predictive accuracy, with criterion-specific performance varying by clinical complexity.
Read MoreThis study assessed generative AI's ability to extract systemic lupus erythematosus classification criteria from medical records. Results showed structured criteria and digital profiling enhance diagnostic accuracy and clinical utility.
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