Artificial Intelligence in Health Care: Bibliometric Analysis

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Abstract

Background

As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly.

Objective

The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications.

Methods

The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software.

Results

The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019.

Conclusions

This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Related collections

Most cited references 36

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Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto … (2018)

Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.

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Prevention and management of chronic disease: a litmus test for health-systems strengthening in low-income and middle-income countries.

Kiran Patel, Tim Evans, Sania Nishtar … (2010)

National health systems need strengthening if they are to meet the growing challenge of chronic diseases in low-income and middle-income countries. By application of an accepted health-systems framework to the evidence, we report that the factors that limit countries' capacity to implement proven strategies for chronic diseases relate to the way in which health systems are designed and function. Substantial constraints are apparent across each of the six key health-systems components of health financing, governance, health workforce, health information, medical products and technologies, and health-service delivery. These constraints have become more evident as development partners have accelerated efforts to respond to HIV, tuberculosis, malaria, and vaccine-preventable diseases. A new global agenda for health-systems strengthening is arising from the urgent need to scale up and sustain these priority interventions. Most chronic diseases are neglected in this dialogue about health systems, despite the fact that non-communicable diseases (most of which are chronic) will account for 69% of all global deaths by 2030 with 80% of these deaths in low-income and middle-income countries. At the same time, advocates for action against chronic diseases are not paying enough attention to health systems as part of an effective response. Efforts to scale up interventions for management of common chronic diseases in these countries tend to focus on one disease and its causes, and are often fragmented and vertical. Evidence is emerging that chronic disease interventions could contribute to strengthening the capacity of health systems to deliver a comprehensive range of services-provided that such investments are planned to include these broad objectives. Because effective chronic disease programmes are highly dependent on well-functioning national health systems, chronic diseases should be a litmus test for health-systems strengthening. Copyright © 2010 Elsevier Ltd. All rights reserved.

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Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan … (2020)

Abstract Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

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Author and article information

Contributors

Fan Yang:

ORCID: https://orcid.org/0000-0003-4250-8446

Social Welfare ProgramSchool of Public AdministrationDongbei University of Finance and Economics217 Jianshan StreetShahekou DistrictDalianChina86 411 84710562fyang10@dufe.edu.cn

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: July 2020

Publication date (Electronic): 29 July 2020

Volume: 22

Issue: 7

Electronic Location Identifier: e18228

Affiliations

[1 ] School of Social Work University of North Carolina at Charlotte Charlotte, NC United States

[2 ] School of Social Work The University of Alabama Tuscaloosa, AL United States

[3 ] Social Welfare Program School of Public Administration Dongbei University of Finance and Economics Dalian China

[4 ] Department of Information Systems University of Maryland Baltimore, MD United States

Author notes

Corresponding Author: Fan Yang fyang10@ 123456dufe.edu.cn

Author information

Yuqi Guo https://orcid.org/0000-0002-7060-8404

Zhichao Hao https://orcid.org/0000-0002-1636-5245

Shichong Zhao https://orcid.org/0000-0001-5964-4853

Jiaqi Gong https://orcid.org/0000-0001-9694-2518

Fan Yang https://orcid.org/0000-0003-4250-8446

Article

Publisher ID: v22i7e18228

DOI: 10.2196/18228

PMC ID: 7424481

PubMed ID: 32723713

SO-VID: cafc2977-4b7c-4d09-a1c9-4c4ddeb2d0f9

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 25 February 2020

Date revision requested : 11 April 2020

Date revision received : 22 April 2020

Date accepted : 14 May 2020

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