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Advances in Kidney Disease and Health Jan 2023Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused... (Review)
Review
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. This article provides an overview of medical artificial intelligence applications relevant to pediatric nephrology. We describe the core concepts of artificial intelligence and machine learning and cover the basics of neural networks and deep learning. We also discuss some examples for clinical applications of artificial intelligence in pediatric nephrology, including neonatal kidney function, early recognition of acute kidney injury, renally cleared drug dosing, intrapatient variability, urinary tract infection workup in infancy, and longitudinal disease progression. Furthermore, we consider the future of artificial intelligence in clinical pediatric nephrology and its potential impact on medical practice and address the ethical issues artificial intelligence raises in terms of clinical decision-making, health care provider-patient relationship, patient privacy, and data collection. This article also represents a call for action involving those of us striving to provide optimal services for children, adolescents, and young adults with chronic conditions.
Topics: Adolescent; Child; Infant, Newborn; Humans; Artificial Intelligence; Nephrology; Neural Networks, Computer; Machine Learning; Renal Dialysis
PubMed: 36723276
DOI: 10.1053/j.akdh.2022.11.001 -
Clinics in Laboratory Medicine Sep 2023In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow... (Review)
Review
In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.
Topics: Artificial Intelligence; Flow Cytometry; Machine Learning
PubMed: 37481325
DOI: 10.1016/j.cll.2023.04.009 -
Yearbook of Medical Informatics Aug 2019This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the... (Review)
Review
OBJECTIVE
This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals.
METHODS
A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email.
RESULTS
The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Fbtient privacy and confidentiality; and 4) HIM workforce training and education.
DISCUSSION
HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional's ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use.
CONCLUSIONS
AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.
Topics: Artificial Intelligence; Delivery of Health Care; Health Information Management; Health Workforce; Medical Informatics; Professional Role
PubMed: 31419816
DOI: 10.1055/s-0039-1677913 -
Texas Heart Institute Journal Mar 2022Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and... (Review)
Review
Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and future of artificial intelligence in education, remote proctoring, credentialing, research, and publication as they pertain to cardiovascular procedures. This review describes the benefits and limitations of artificial intelligence and machine learning and the exciting potential of integrating advanced simulation, holography, virtual reality, and extended reality into disease diagnosis and patient care, as well as their roles in cardiovascular research and education. Nonetheless, much of the available data resides in electronic medical records or within industry-sponsored proprietary programs that are not compatible or standardized for current clinical application. Many areas in cardiovascular medicine would benefit from the introduction or increased use of artificial intelligence. Web-based artificial intelligence applications could be used to address unmet needs for education, on-demand procedural proctoring, credentialing, and recredentialing for interventionists and physicians in remote locations. Further progress in artificial intelligence will require further collaboration among computer scientists and researchers in order to identify and correct the most relevant problems and to implement the best data-based approach to achieving this goal. The future success of artificial intelligence in cardiovascular medicine will depend on the degree of collaboration between all pertinent experts in this field. This will undoubtedly be a prolonged, stepwise process.
Topics: Artificial Intelligence; Credentialing; Forecasting; Humans; Machine Learning
PubMed: 35481865
DOI: 10.14503/THIJ-21-7572 -
Veterinary Radiology & Ultrasound : the... Dec 2022The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides... (Review)
Review
The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides essential definitions of AI with medical images with a focus on veterinary radiology. Machine learning methods common in medical image analysis are compared, and a detailed description of convolutional neural networks commonly used in deep learning classification and regression models is provided. A brief introduction to natural language processing (NLP) and its utility in machine learning is also provided. NLP can economize the creation of "truth-data" needed when training AI systems for both diagnostic radiology and radiation oncology applications. The goal of this publication is to provide veterinarians, veterinary radiologists, and radiation oncologists the necessary background needed to understand and comprehend AI-focused research projects and publications.
Topics: Animals; Humans; Artificial Intelligence; Deep Learning; Diagnostic Imaging; Machine Learning; Radiology
PubMed: 36514230
DOI: 10.1111/vru.13160 -
The American Journal of Pathology Oct 2021
Topics: Artificial Intelligence; Humans; Machine Learning; Neural Networks, Computer; Pathology
PubMed: 34391718
DOI: 10.1016/j.ajpath.2021.07.011 -
Digestive Endoscopy : Official Journal... Jan 2021Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes... (Review)
Review
Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.
Topics: Artificial Intelligence; Capsule Endoscopy; Deep Learning; Humans; Intestine, Small; Machine Learning
PubMed: 33211357
DOI: 10.1111/den.13896 -
Journal of Clinical Ultrasound : JCU Nov 2022Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest... (Review)
Review
Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.
Topics: Humans; Artificial Intelligence; Deep Learning; Machine Learning; Image Processing, Computer-Assisted; Musculoskeletal System
PubMed: 36069404
DOI: 10.1002/jcu.23321 -
BMJ (Clinical Research Ed.) Apr 2020
Topics: Artificial Intelligence; Deep Learning; Reference Standards
PubMed: 32245846
DOI: 10.1136/bmj.m1326 -
Journal of Thrombosis and Haemostasis :... Apr 2023
Topics: Humans; Artificial Intelligence; Machine Learning
PubMed: 36990517
DOI: 10.1016/j.jtha.2023.01.026