AI Article Synopsis
- AI is revolutionizing healthcare, particularly in nephrology, by improving the early detection, diagnosis, prognosis, and treatment of kidney diseases.
- Many clinical AI studies in nephrology lack consistent reporting standards, making it hard to interpret and apply their findings in everyday practice.
- Global initiatives are proposing guidelines for AI research reporting to enhance reproducibility and ethical use, which will ultimately improve patient care and clinical decision-making.
Article Abstract
The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968354 | PMC |
| http://dx.doi.org/10.3390/biomedicines12030606 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Soybean2035: A decadal vision for soybean functional genomics and breeding.
Mol Plant
January 2025
Center for Applied Genetic Technologies, University of Georgia, Athens, USA.
Soybean, the fourth most important crop in the world, uniquely serves as a source of both plant oil and plant protein for the world's food and animal feed. Although soybean production has increased approximately 13-fold over the past 60 years, the continually growing global population necessitates further increases in soybean production. In the past, especially in the last decade, significant progress has been made in both functional genomics and molecular breeding.
View Article and Find Full Text PDFIntegrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.
Viruses
November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
View Article and Find Full Text PDFVaccine Strategies Against RNA Viruses: Current Advances and Future Directions.
Vaccines (Basel)
November 2024
Research Center for Emerging Viral Infections, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan.
The development of vaccines against RNA viruses has undergone a rapid evolution in recent years, particularly driven by the COVID-19 pandemic. This review examines the key roles that RNA viruses, with their high mutation rates and zoonotic potential, play in fostering vaccine innovation. We also discuss both traditional and modern vaccine platforms and the impact of new technologies, such as artificial intelligence, on optimizing immunization strategies.
View Article and Find Full Text PDFReal-Time Freezing of Gait Prediction and Detection in Parkinson's Disease.
Sensors (Basel)
December 2024
School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson's disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 ( = 11) was collected in a previous study.
View Article and Find Full Text PDFEvaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence.
Sensors (Basel)
December 2024
LAPLACE Laboratory-UMR5213, National Polytechnic Institute of Toulouse, 31077 Toulouse, France.
This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!