AI Medical Compendium

Spatial AI consistently preferred to state-of-the-art hearing aids in multitalker noise.

International journal of audiology May 11, 2026

OBJECTIVE: We examined the Spatial AI model running on the Fortell AI hearing aids to see whether it improves perceived ease of understanding in noisy, multitalker environments relative to hearing aids using more traditional processing. DESIGN: In a ... read more

Natural Language Processing for Automated Classification of Cleft and Craniofacial Procedures From Operative Notes: Model Development and Feasibility Study.

JMIR medical informatics May 11, 2026

BACKGROUND: The accurate classification of operative notes is essential for surgical outcomes research; however, CPT code classification is notoriously nonspecific for many procedures. In such situations, the operative note (or "dictation") must be r... read more

Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks.

Proceedings of the National Academy of Sciences of the United States of America May 11, 2026

In vivo microscopy (IVM) has shown great promise to improve early detection of epithelial precancer, but it suffers from fundamental trade-offs that limit the resolution, field-of-view (FOV) and depth-of-field (DOF). Here, we present PrecisionView, a... read more

Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study.

Journal of medical Internet research May 11, 2026

BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leveraging electronic health records have emerged as promising tools for suicide risk identification, the o... read more

Electrically Reconfigurable Floating Gate Optoelectronic Synaptic Pixels for In-sensor Convolutional Image Feature Extraction with Built-in Contrast Enhancement.

ACS nano May 11, 2026

Image feature extraction and enhancement are fundamental operations in real-time object detection using convolutional neural networks (CNNs). In conventional architectures, continuous data transfer between sensors, memory, and processing units leads ... read more

Expert Evaluation and Consensus on GPT-4o Summaries of Clinical Letters: Validation and Results of the Framework and Implementation of AI Tools Project.

JMIR medical informatics May 11, 2026

BACKGROUND: Large language models (LLMs) are increasingly used to summarize clinical documents; yet, automated metrics often inadequately capture clinical relevance and safety. In the initial phase of the "Framework and Implementation of AI Tools," a... read more

Early Identification of Mobility Limitations in Community-Dwelling Middle-Aged and Older Adults: Development of a Prediction Model Based on a Prospective Cohort.

JMIR aging May 11, 2026

BACKGROUND: With the aging of the global population, preventing the onset of mobility limitations is considered a worldwide public health priority. OBJECTIVE: This study aimed to develop a predictive model for incident early mobility limitations (EML... read more

DynaTOF: Time-Resolved Noncontrast Cerebral MR Angiography Using Spatially Modulated RF Saturation.

Magnetic resonance in medicine May 11, 2026

PURPOSE: To develop a time-resolved extension of three-dimensional time-of-flight MR angiography, termed Dynamic TOF (DynaTOF), that exploits intrinsic RF saturation behavior to enable noncontrast visualization of cerebrovascular hemodynamics. METHOD... read more

Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help.

JMIR AI May 11, 2026

Developing effective health care teams is critical to meet the rising complexity in patient care. However, optimizing team composition, interpersonal dynamics, and care processes in complex health care systems requires processing vast amounts of data... read more

Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning.

JMIR mHealth and uHealth May 11, 2026

BACKGROUND: Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction ... read more

Artificial Intelligence Medical Compendium