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Comparison of the Accuracy, Completeness, Reproducibility, and Consistency of Different AI Chatbots in Providing Nutritional Advice: An Exploratory Study.
J Clin Med
December 2024
Department of Medical Science, University of Turin, 10126 Torino, Italy.
: The use of artificial intelligence (AI) chatbots for obtaining healthcare advice is greatly increased in the general population. This study assessed the performance of general-purpose AI chatbots in giving nutritional advice for patients with obesity with or without multiple comorbidities. : The case of a 35-year-old male with obesity without comorbidities (Case 1), and the case of a 65-year-old female with obesity, type 2 diabetes mellitus, sarcopenia, and chronic kidney disease (Case 2) were submitted to 10 different AI chatbots on three consecutive days.
View Article and Find Full Text PDFArtificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review.
J Clin Med
December 2024
Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 010000 Astana, Kazakhstan.
Mechanical ventilation (MV) is one of the most frequently used organ replacement modalities in the intensive care unit (ICU). Artificial intelligence (AI) presents substantial potential in optimizing mechanical ventilation management. The utility of AI in MV lies in its ability to harness extensive data from electronic monitoring systems, facilitating personalized care tailored to individual patient needs.
View Article and Find Full Text PDFEnhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images.
Bioengineering (Basel)
November 2024
Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
We assessed the feasibility of using deep learning-based image harmonization to improve the reproducibility of radiomics features in abdominal CT scans. In CT imaging, harmonization adjusts images from different institutions to ensure consistency despite variations in scanners and acquisition protocols. This process is essential because such differences can lead to variability in radiomics features, affecting reproducibility and accuracy.
View Article and Find Full Text PDFUnlocking new possibility of FeO@C@Ag nanostructures as an advanced SERS substrate for ultrasensitive detection of low-cross-section urea biomolecules.
RSC Adv
January 2025
Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
Surface-enhanced Raman spectroscopy (SERS) is widely recognized as a powerful analytical technique, offering molecular identification by amplifying characteristic vibrational signals, even at the single-molecule level. While SERS has been successfully applied for a wide range of targets including pesticides, dyes, bacteria, and pharmaceuticals, it has struggled with the detection of molecules with inherently low Raman scattering cross-sections. Urea, a key nitrogen-containing biomolecule and the diamide of carbonic acid, is a prime example of such a challenging target.
View Article and Find Full Text PDFBackground: Patients with chronic kidney disease (CKD) have serum, bone, and vascular abnormalities presenting as chronic kidney disease-mineral bone disorder (CKD-MBD) syndrome. This study sought to identify the parameters with the greatest relative impact on progression of CKD-MBD abnormalities.
Materials And Methods: This prospective study measured 237 parameters including serum markers, clinical variables, dual-energy X-ray absorptiometry (DXA) measurements, vascular calcifications, and histomorphometric results from bone samples obtained at baseline and after 2 - 3 years.
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