Purpose Of Review: Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management.
Recent Findings: We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
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http://dx.doi.org/10.1007/s11882-024-01152-y | DOI Listing |
J Ovarian Res
January 2025
Department of Urology, Zigong Fourth People's Hospital, Zigong, Sichuan, China.
Background: Granulosa cell proliferation and survival are essential for normal ovarian function and follicular development. Long non-coding RNAs (lncRNAs) have emerged as important regulators of cell proliferation and differentiation. Nuclear paraspeckle assembly transcript 1 (NEAT1) has been implicated in various cellular processes, but its role in granulosa cell function remains unclear.
View Article and Find Full Text PDFVet Res
January 2025
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.
View Article and Find Full Text PDFBMC Res Notes
January 2025
UQ Centre for Clinical Research, Faculty of Health Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Australia.
Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.
Data Description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020.
J Transl Med
January 2025
Department of Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Background: Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers.
View Article and Find Full Text PDFBMC Med Educ
January 2025
School of Acupuncture and Tuina, Chengdu University of TCM, Chengdu, China.
Background: Artificial intelligence has gradually been used into various fields of medical education at present. Under the background of moxibustion robot teaching assistance, the study aims to explore the relationship and the internal mechanism between learning engagement and evaluation in three stages, preparation before class, participation in class, and consolidation after class.
Methods: Based on the data investigated in 250 youths in university via multistage cluster sampling following the self-administered questionnaire, structural equation model was built to discussing factors of study process about moxibustion robots.
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