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Diagnostics
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Artificial Intelligence in Metabolic Diseases
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Artificial Intelligence in Metabolic Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1970

Special Issue Editor

Dr. Md Mohaimenul Islam

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
Interests: machine learning; deep learning; artificial intelligence; medicine; meta-analysis; clinical decision support system; evidence-based medicine; pharmacoepidemiology; cancer; observational study; retrospective study
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolic disorders encompass a spectrum of health conditions that pose significant global health challenges. Previous studies have underscored their impact on human well-being, emerging as a predominant cause of mortality worldwide. Recent advancements in artificial intelligence (AI) have sparked optimism for enhancing healthcare outcomes. Indeed, AI methodologies are increasingly being leveraged for early risk assessment and diagnosis of serious or complex metabolic diseases. The integration of AI into health data analysis is reshaping traditional approaches to disease diagnosis, prevention, and the care of individuals with a higher risk of metabolic diseases. The rise of AI within the realm of big data showcases its potential to increase the efficiency and precision of medical practitioners' endeavors. This Special Issue endeavors to delve into the pivotal role played by AI in addressing the challenges presented by metabolic diseases.

Dr. Md Mohaimenul Islam
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Review

15 pages, 1409 KiB  
Review
Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research
by Chieh-Chen Wu, Md. Mohaimenul Islam, Tahmina Nasrin Poly and Yung-Ching Weng
Diagnostics 2024, 14(4), 397; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics14040397 - 12 Feb 2024
Viewed by 1744
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric [...] Read more.
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain. Full article
(This article belongs to the Special Issue Artificial Intelligence in Metabolic Diseases)
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