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There is growing interest in using artificial intelligence (AI) to track the outcomes of patients with facial paralysis (FP). However, validation studies of the AI output with other outcome measurements are still scarce in the literature. We aimed to correlate an AI-derived emotionality state score (ESS) with layperson assessment and objective oral commissure excursion in facial reanimation outcomes.

Major depressive disorder (MDD) in U.S. adults is common, disabling, and costly. Screening is recommended when follow-up is available, yet uptake remains uneven, and digital tools raise governance risks.

Artificial intelligence (AI) -based, mammography breast cancer (bc) risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of three clinical bc risk models-Gail, Tyrer-Cuzick (TC) v8, and Breast Cancer Surveillance Consortium (BCSC) v3 - to the MIRAI AI-risk model.

Diagnostic reasoning, the cognitive process of interpreting clinical information to arrive at a diagnosis, is a critical competency in medical education, and has been taught using authentic methods such as standardized patients and peer role-playing that simulate real clinical encounters. Although effective, these approaches require substantial time, space, and cost.

Aging is characterized by a decline in essential sensory functions, including olfaction, which is crucial for environmental interaction and survival. This decline is often paralleled by the cellular accumulation of dysfunctional mitochondria, particularly detrimental in post-mitotic cells such as neurons. Mitochondrial stress triggers the mitochondrial unfolded protein response (UPR