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Can we use lower extremity joint moments predicted by the artificial intelligence model during walking in patients with cerebral palsy in the clinical gait analysis?

PloS one
Several studies have highlighted the advantages of employing artificial intelligence (AI) models in gait analysis. However, the credibility and practicality of integrating these models into clinical gait routines remain uncertain. This study critical...

A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...

Detection of antibodies in suspected autoimmune encephalitis diseases using machine learning.

Scientific reports
In our study, we aim to predict the antibody serostatus of patients with suspected autoimmune encephalitis (AE) using machine learning based on pre-contrast T2-weighted MR images acquired at symptom onset. A confirmation of seropositivity is of great...

People are poorly equipped to detect AI-powered voice clones.

Scientific reports
As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on ...

Online Health Information-Seeking in the Era of Large Language Models: Cross-Sectional Web-Based Survey Study.

Journal of medical Internet research
BACKGROUND: As large language model (LLM)-based chatbots such as ChatGPT (OpenAI) grow in popularity, it is essential to understand their role in delivering online health information compared to other resources. These chatbots often generate inaccura...

Using machine learning to identify features associated with different types of self-injurious behaviors in autistic youth.

Psychological medicine
BACKGROUND: Self-injurious behaviors (SIB) are common in autistic people. SIB is mainly studied as a broad category, rather than by specific SIB types. We aimed to determine associations of distinct SIB types with common psychiatric, emotional, medic...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

Hearing research
The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predict...

Investigation of the relationship between medical artificial intelligence readiness and individual innovativeness levels in nursing students.

Nurse education today
AIM: This study was conducted to identify nursing students' medical artificial intelligence readiness and individual innovativeness levels, to examine the relationship between these two concepts and to determine the variables that create a significan...

Comparing discriminatory behavior against AI and humans.

Scientific reports
Although discrimination is typically believed to occur from well-defined categories like ethnicity, disability, and sex, studies have found that discrimination persists in minimal conditions lacking such categories. Participants have been found to pr...

IdenBAT: Disentangled representation learning for identity-preserved brain age transformation.

Artificial intelligence in medicine
Brain age transformation aims to convert reference brain images into synthesized images that accurately reflect the age-specific features of a target age group. The primary objective of this task is to modify only the age-related attributes of the re...