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Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study.

Scientific reports
This study aimed to explore potential risk factors for severe endometriosis and to develop a model to predict the risk of severe endometriosis. A total of 308 patients with endometriosis were analyzed. Least absolute shrinkage and selection operator ...

Reflective writing assignments in the era of GenAI: student behavior and attitudes suggest utility, not futility.

Advances in physiology education
Reflective writing is widely used in health sciences education, but overreliance on generative artificial intelligence (GenAI) could undermine the reflective writing process. To explore this concern, students in three undergraduate courses with refle...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...

Knowledge and use, perceptions of benefits and limitations of artificial intelligence chatbots among Italian physiotherapy students: a cross-sectional national study.

BMC medical education
BACKGROUND: Artificial Intelligence (AI) Chatbots (e.g., ChatGPT, Microsoft Bing, and Google Bard) can emulate human interaction and may support physiotherapy education. Despite growing interest, physiotherapy students' perspectives remain unexplored...

MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

Malaria journal
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...

A machine learning approach to investigate the role of fear of pain, personal experience, and vicarious learning in dental anxiety.

BMC oral health
BACKGROUND: Dental anxiety is a pervasive problem worldwide, leading to avoidance of dental care, resulting in oral health problems and impacting daily life through social withdrawal and work absenteeism. Addressing this fear is an important public h...

Exploring the influence of artificial intelligence integration on personalized learning: a cross-sectional study of undergraduate medical students in the United Kingdom.

BMC medical education
BACKGROUND: With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI...

Can humanoid robots be used as a cognitive offloading tool?

Cognitive research: principles and implications
Cognitive load occurs when the demands of a task surpass the available processing capacity, straining mental resources and potentially impairing performance efficiency, such as increasing the number of errors in a task. Owing to its ubiquity in real-...

Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.

PloS one
The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study han...

Barriers to the widespread adoption of diagnostic artificial intelligence for preventing antimicrobial resistance.

Scientific reports
Currently, antimicrobial resistance (AMR) poses a major public health challenge. The emergence of AMR, which significantly threatens public health, is primarily due to the overuse of antimicrobial agents. This study explored the possibility that the ...