AIMC Topic: Humans

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Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.

JMIR formative research
BACKGROUND: Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior research h...

Beyond Lectures: Reimagining Psychiatric Didactics for the Age of AI.

JMIR medical education
The increasing use of generative large language models (LLMs) necessitates a fundamental reevaluation of traditional didactic lectures in medical education, particularly within psychiatry. The specialty's inherent diagnostic ambiguity, biopsychosocia...

Digital Health Technology Compliance With Clinical Safety Standards In the National Health Service in England: National Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: To be authorized for use in the National Health Service (NHS) in England, digital health technologies (DHTs) must meet 2 mandatory clinical risk management standards, Data Coordination Board (DCB) 0129 and 0160, demonstrating that risks f...

Discovering sensorimotor agency in cellular automata using diversity search.

Science advances
The field of artificial life studies how life-like phenomena such as agency and self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open question is whether it is possible to find environment rules that self-or...

An AI-Supported Methodology for Analyzing Deflections and Misalignments in Human Interactions.

Integrative psychological & behavioral science
This paper introduces Speech Act Deflection and Misalignment Analysis (SADMA), an AI-assisted methodology for identifying conversational misalignments that reveal underlying interpersonal dynamics. Grounded in a "meaning-as-a-response" framework-comb...

Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics.

Clinical and experimental medicine
OBJECTIVE: To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabeti...

The ferroptosis-related gene MAFG screened by machine learning is associated with the diagnosis and prognosis of sepsis.

Clinical and experimental medicine
Ferroptosis is a novel form of cell death induced by ferrous ions and lipid peroxidation. However, the mechanisms of ferroptosis-related genes (FRGs) in sepsis have not been studied thoroughly. We performed differential analysis using GSE65682, and t...

Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.

Journal of neuro-oncology
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...

Identification of potential biomarkers for Lyme disease using bioinformatics and machine learning.

Clinical and experimental medicine
Lyme disease (LD) presents significant diagnostic challenges due to the absence of a reliable screening method for initial detection. This study aimed to identify potential biomarkers using bioinformatics and machine learning algorithms, which may co...

A review of the application of deep learning in thyroid nodule imaging: from model architectures to training methods and core image analysis tasks.

Biomedical physics & engineering express
Thyroid nodules are highly prevalent in clinical practice, and their incidence has been steadily increasing in recent years, posing significant threats to human health. Traditional imaging examinations for thyroid nodules rely heavily on physicians' ...