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Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340656 | PMC |
http://dx.doi.org/10.3390/diagnostics13132308 | DOI Listing |
J Eval Clin Pract
September 2025
Department of Orthopedics and Traumatology, Medical Faculty, University of Health Sciences, Antalya, Turkey.
Aims And Objective: The field of medical statistics has experienced significant advancements driven by integrating innovative statistical methodologies. This study aims to conduct a comprehensive analysis to explore current trends, influential research areas, and future directions in medical statistics.
Methods: This paper maps the evolution of statistical methods used in medical research based on 4,919 relevant publications retrieved from the Web of Science.
Curr Opin Neurol
October 2025
Friedrich-Baur-Institute, Department of Neurology, LMU Clinic, Munich, Germany.
Zhong Nan Da Xue Xue Bao Yi Xue Ban
May 2025
Department of Geriatric Pulmonary and Critical Care Medicine, Xiangya Hospital, Central South University; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha 410008.
Objectives: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the and genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility.
View Article and Find Full Text PDFDermatitis
September 2025
From the Department of Dermatology, Venereology and Leprology, All India Institute of Medical Sciences (AIIMS), Bhopal, India.
Contact dermatitis (CD), which includes both allergic CD and irritant CD, is a common inflammatory condition that can pose significant diagnostic challenges. Although patch testing is the gold standard for identifying causative allergens for allergic contact dermatitis (ACD), it is time-consuming, subjective, and requires expert interpretation. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning, have shown promise in improving the accuracy, efficiency, and accessibility of CD diagnosis and management.
View Article and Find Full Text PDFDalton Trans
September 2025
School of Education, Can Tho University, 3-2 Road, Can Tho City 900000, Vietnam.
Enhancement of the performance of lithium-ion batteries is a critical strategy for addressing the challenges associated with cost and raw materials. By doping boron (B), aluminum (Al), and aluminum/boron (Al/B) utilizing the sol-gel method, we demonstrate a substantial improvement in the cycling performance of Ni-rich lithium nickel manganese cobalt oxide (NMC) as an electrode. While the initial specific capacitance of the doped samples may be lower than that of the pristine NMC, these samples demonstrate a notable increase in specific capacitance during subsequent cycles, reaching a peak around the 10 cycle and nearing the highest specific capacitance observed in NMC cathodes.
View Article and Find Full Text PDF