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医疗保健研究中人工智能的文献计量分析:趋势与未来方向。

Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions.

作者信息

Senthil Renganathan, Anand Thirunavukarasou, Somala Chaitanya Sree, Saravanan Konda Mani

机构信息

Department of Bioinformatics, School of Lifesciences, Vels Institute of Science Technology and Advanced Studies (VISTAS), Pallavaram, Chennai 600117, Tamil Nadu, India.

SRIIC Lab, Faculty of Clinical Research, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nadu, India.

出版信息

Future Healthc J. 2024 Sep 3;11(3):100182. doi: 10.1016/j.fhj.2024.100182. eCollection 2024 Sep.


DOI:10.1016/j.fhj.2024.100182
PMID:39310219
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11414662/
Abstract

OBJECTIVE: The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source. METHODS: Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis. RESULTS: The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation. CONCLUSION: This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.

摘要

目的:人工智能(AI)在医疗保健领域的出现是一股强大且具有变革性的力量,正在彻底改变整个行业。借助复杂的算法和数据分析,人工智能在改善患者护理、提高运营效率以及促进整个医疗生态系统的创新方面具有无与伦比的前景。本研究以SCOPUS数据库作为主要数据源,对医疗保健领域中人工智能的研究进行了全面的文献计量分析。 方法:2013年的初步研究结果确定了153篇关于人工智能与医疗保健的出版物。在2019年至2023年期间,出版物数量呈指数级增长,表明该领域取得了显著的发展。该分析采用了各种文献计量指标来评估研究产出表现、科学图谱技术和主题图谱分析。 结果:该研究揭示了人工智能与医疗保健研究中的热点、主题重点和新兴趋势。基于对Scopus数据库的广泛审查提供了简要概述,并提出了进一步调查的潜在途径。 结论:本文为理解医疗保健领域人工智能的当前格局做出了有价值的贡献,为未来的研究方向提供了见解,并为该领域的战略决策提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/6f47934b7979/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/ae63f4894a2e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/d9a2fa7526d3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/fdc930481b66/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/23dac543d13f/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/2a900fe60e15/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/33d81475ce8d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/953bcac97c83/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/772de2267c5a/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/6f47934b7979/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/ae63f4894a2e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/d9a2fa7526d3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/fdc930481b66/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/23dac543d13f/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/2a900fe60e15/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/33d81475ce8d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/953bcac97c83/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/772de2267c5a/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/11414662/6f47934b7979/gr9.jpg

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[2]
Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

Brief Bioinform. 2024-5-23

[3]
Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.

JAMIA Open. 2024-5-24

[4]
Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.

Life (Basel). 2024-4-26

[5]
PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.

Nat Cancer. 2024-6

[6]
Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors.

Methods. 2023-11

[7]
Revolutionizing healthcare: the role of artificial intelligence in clinical practice.

BMC Med Educ. 2023-9-22

[8]
Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm.

Front Artif Intell. 2023-8-29

[9]
An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C).

J Am Med Inform Assoc. 2023-11-17

[10]
A Review of the Role of Artificial Intelligence in Healthcare.

J Pers Med. 2023-6-5

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