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Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.

作者信息

Stafie Celina Silvia, Sufaru Irina-Georgeta, Ghiciuc Cristina Mihaela, Stafie Ingrid-Ioana, Sufaru Eduard-Constantin, Solomon Sorina Mihaela, Hancianu Monica

机构信息

Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania.

Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania.

出版信息

Diagnostics (Basel). 2023 Jun 7;13(12):1995. doi: 10.3390/diagnostics13121995.


DOI:10.3390/diagnostics13121995
PMID:37370890
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10297646/
Abstract

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/b3456749b3c9/diagnostics-13-01995-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/5cda1cd7c641/diagnostics-13-01995-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/5db0a94640eb/diagnostics-13-01995-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/cfb38d3611f7/diagnostics-13-01995-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/b3456749b3c9/diagnostics-13-01995-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/5cda1cd7c641/diagnostics-13-01995-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/5db0a94640eb/diagnostics-13-01995-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/cfb38d3611f7/diagnostics-13-01995-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0822/10297646/b3456749b3c9/diagnostics-13-01995-g004.jpg

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本文引用的文献

[1]
Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

JMIR AI. 2023-1-16

[2]
Assessment and quantification of ovarian reserve on the basis of machine learning models.

Front Endocrinol (Lausanne). 2023

[3]
Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review.

PLOS Digit Health. 2022-3-31

[4]
Development, Validation and Comparison of Artificial Neural Network and Logistic Regression Models Predicting Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.

Allergy Asthma Immunol Res. 2023-1

[5]
Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review.

Cardiovasc Diabetol. 2023-1-19

[6]
Explainable artificial intelligence for mental health through transparency and interpretability for understandability.

NPJ Digit Med. 2023-1-18

[7]
Development, Application, and Performance of Artificial Intelligence in Cephalometric Landmark Identification and Diagnosis: A Systematic Review.

Healthcare (Basel). 2022-12-5

[8]
Genetic algorithm-assisted machine learning for clinical pregnancy prediction in in vitro fertilization.

AJOG Glob Rep. 2022-11-9

[9]
Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.

Front Immunol. 2022

[10]
Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis.

Comput Intell Neurosci. 2022

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