Artificial intelligence for healthcare in Africa: a scientometric analysis
Introduction Artificial intelligence (AI) has greatly transformed healthcare in developed countries. However, there is limited data describing the extent of AI adoption in African healthcare systems. The aim of this study was to understand the state of AI healthcare research in Africa. Methods A sci...
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| Veröffentlicht in: | Health and technology 2023-11, Vol.13 (6), p.947-955 |
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| creator | Njei, Basile Kanmounye, Ulrick Sidney Mohamed, Mouhand F. Forjindam, Anim Ndemazie, Nkafu Bechem Adenusi, Adedeji Egboh, Stella-Maris C. Chukwudike, Evaristus S. Monteiro, Joao Filipe G. Berzin, Tyler M. Asombang, Akwi W. |
| description | Introduction
Artificial intelligence (AI) has greatly transformed healthcare in developed countries. However, there is limited data describing the extent of AI adoption in African healthcare systems. The aim of this study was to understand the state of AI healthcare research in Africa.
Methods
A scientometric analysis was conducted to visualize the state-of-the-art research of AI in healthcare in Africa.
Results
Twenty-six relevant articles, published by 178 authors and affiliated with 96 organizations in 31 countries, were included. The most prolific African countries were South Africa, followed by Nigeria and Ghana. Some articles were published by authors affiliated with non-African countries. None of the contributing authors published more than 2 articles. Only 20 (11.2%) authors collaborated, forming a single cluster. The most common AI tools used in African health systems were deep learning neural networks applied in medical imaging, Adaptive Neuro-Fuzzy Inference Systems, and E-algorithms.
Conclusion
Our results suggest that AI for healthcare in Africa is still in its developmental phase with limited published research. Our social network analysis highlighted a South and West African predominance in the research relational network of AI in healthcare. This discrepancy presents an opportunity for coordination and increased collaboration with healthcare institutions advanced in the use of AI within Africa and beyond. |
| doi_str_mv | 10.1007/s12553-023-00786-8 |
| format | Article |
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Artificial intelligence (AI) has greatly transformed healthcare in developed countries. However, there is limited data describing the extent of AI adoption in African healthcare systems. The aim of this study was to understand the state of AI healthcare research in Africa.
Methods
A scientometric analysis was conducted to visualize the state-of-the-art research of AI in healthcare in Africa.
Results
Twenty-six relevant articles, published by 178 authors and affiliated with 96 organizations in 31 countries, were included. The most prolific African countries were South Africa, followed by Nigeria and Ghana. Some articles were published by authors affiliated with non-African countries. None of the contributing authors published more than 2 articles. Only 20 (11.2%) authors collaborated, forming a single cluster. The most common AI tools used in African health systems were deep learning neural networks applied in medical imaging, Adaptive Neuro-Fuzzy Inference Systems, and E-algorithms.
Conclusion
Our results suggest that AI for healthcare in Africa is still in its developmental phase with limited published research. Our social network analysis highlighted a South and West African predominance in the research relational network of AI in healthcare. This discrepancy presents an opportunity for coordination and increased collaboration with healthcare institutions advanced in the use of AI within Africa and beyond.</description><identifier>ISSN: 2190-7188</identifier><identifier>EISSN: 2190-7196</identifier><identifier>DOI: 10.1007/s12553-023-00786-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptive systems ; Algorithms ; Artificial intelligence ; Artificial neural networks ; Bibliometrics ; Biological and Medical Physics ; Biomedical Engineering and Bioengineering ; Biomedicine ; Biophysics ; Citation analysis ; Citation management software ; Collaboration ; Computational Biology/Bioinformatics ; Electronic health records ; Engineering ; Fuzzy logic ; Fuzzy systems ; Health care ; Machine learning ; Medical imaging ; Medicine/Public Health ; Metadata ; Network analysis ; Original Paper ; R & D/Technology Policy ; Scientometrics ; Social networks</subject><ispartof>Health and technology, 2023-11, Vol.13 (6), p.947-955</ispartof><rights>This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023</rights><rights>This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-39823cd6d288efc91d47334152b3c96895438a823caecf67601403a8877c282e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12553-023-00786-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919478558?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Njei, Basile</creatorcontrib><creatorcontrib>Kanmounye, Ulrick Sidney</creatorcontrib><creatorcontrib>Mohamed, Mouhand F.</creatorcontrib><creatorcontrib>Forjindam, Anim</creatorcontrib><creatorcontrib>Ndemazie, Nkafu Bechem</creatorcontrib><creatorcontrib>Adenusi, Adedeji</creatorcontrib><creatorcontrib>Egboh, Stella-Maris C.</creatorcontrib><creatorcontrib>Chukwudike, Evaristus S.</creatorcontrib><creatorcontrib>Monteiro, Joao Filipe G.</creatorcontrib><creatorcontrib>Berzin, Tyler M.</creatorcontrib><creatorcontrib>Asombang, Akwi W.</creatorcontrib><title>Artificial intelligence for healthcare in Africa: a scientometric analysis</title><title>Health and technology</title><addtitle>Health Technol</addtitle><description>Introduction
Artificial intelligence (AI) has greatly transformed healthcare in developed countries. However, there is limited data describing the extent of AI adoption in African healthcare systems. The aim of this study was to understand the state of AI healthcare research in Africa.
Methods
A scientometric analysis was conducted to visualize the state-of-the-art research of AI in healthcare in Africa.
Results
Twenty-six relevant articles, published by 178 authors and affiliated with 96 organizations in 31 countries, were included. The most prolific African countries were South Africa, followed by Nigeria and Ghana. Some articles were published by authors affiliated with non-African countries. None of the contributing authors published more than 2 articles. Only 20 (11.2%) authors collaborated, forming a single cluster. The most common AI tools used in African health systems were deep learning neural networks applied in medical imaging, Adaptive Neuro-Fuzzy Inference Systems, and E-algorithms.
Conclusion
Our results suggest that AI for healthcare in Africa is still in its developmental phase with limited published research. Our social network analysis highlighted a South and West African predominance in the research relational network of AI in healthcare. This discrepancy presents an opportunity for coordination and increased collaboration with healthcare institutions advanced in the use of AI within Africa and beyond.</description><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Bibliometrics</subject><subject>Biological and Medical Physics</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Biophysics</subject><subject>Citation analysis</subject><subject>Citation management software</subject><subject>Collaboration</subject><subject>Computational Biology/Bioinformatics</subject><subject>Electronic health records</subject><subject>Engineering</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Health care</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Medicine/Public Health</subject><subject>Metadata</subject><subject>Network analysis</subject><subject>Original Paper</subject><subject>R & D/Technology Policy</subject><subject>Scientometrics</subject><subject>Social networks</subject><issn>2190-7188</issn><issn>2190-7196</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1LAzEQhoMoWLR_wNOC59V8bDYTb6X4ScGLnkNMJ23Kdrcm6aH_3tQVvTkwzDDzvMPwEnLF6A2jVN0mxqUUNeUlqYK2hhMy4UzTWjHdnv72AOdkmtKGlpBM6kZMyMss5uCDC7arQp-x68IKe4eVH2K1RtvltbMRy66a-RicvatslVzAPg9bzGVS2d52hxTSJTnztks4_akX5P3h_m3-VC9eH5_ns0XtuKK5Fhq4cMt2yQHQO82WjRKiYZJ_CKdb0LIRYI-MRedb1VLWUGEBlHIcOIoLcj3e3cXhc48pm82wj-WJZLhmulEgJRSKj5SLQ0oRvdnFsLXxYBg1R9vMaJsptplv28xRJEZRKnC_wvh3-h_VF0oqbnk</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Njei, Basile</creator><creator>Kanmounye, Ulrick Sidney</creator><creator>Mohamed, Mouhand F.</creator><creator>Forjindam, Anim</creator><creator>Ndemazie, Nkafu Bechem</creator><creator>Adenusi, Adedeji</creator><creator>Egboh, Stella-Maris C.</creator><creator>Chukwudike, Evaristus S.</creator><creator>Monteiro, Joao Filipe G.</creator><creator>Berzin, Tyler M.</creator><creator>Asombang, Akwi W.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M0T</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20231101</creationdate><title>Artificial intelligence for healthcare in Africa: a scientometric analysis</title><author>Njei, Basile ; Kanmounye, Ulrick Sidney ; Mohamed, Mouhand F. ; Forjindam, Anim ; Ndemazie, Nkafu Bechem ; Adenusi, Adedeji ; Egboh, Stella-Maris C. ; Chukwudike, Evaristus S. ; Monteiro, Joao Filipe G. ; Berzin, Tyler M. ; Asombang, Akwi W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-39823cd6d288efc91d47334152b3c96895438a823caecf67601403a8877c282e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Bibliometrics</topic><topic>Biological and Medical Physics</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Biophysics</topic><topic>Citation analysis</topic><topic>Citation management software</topic><topic>Collaboration</topic><topic>Computational Biology/Bioinformatics</topic><topic>Electronic health records</topic><topic>Engineering</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Health care</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Medicine/Public Health</topic><topic>Metadata</topic><topic>Network analysis</topic><topic>Original Paper</topic><topic>R & D/Technology Policy</topic><topic>Scientometrics</topic><topic>Social networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Njei, Basile</creatorcontrib><creatorcontrib>Kanmounye, Ulrick Sidney</creatorcontrib><creatorcontrib>Mohamed, Mouhand F.</creatorcontrib><creatorcontrib>Forjindam, Anim</creatorcontrib><creatorcontrib>Ndemazie, Nkafu Bechem</creatorcontrib><creatorcontrib>Adenusi, Adedeji</creatorcontrib><creatorcontrib>Egboh, Stella-Maris C.</creatorcontrib><creatorcontrib>Chukwudike, Evaristus S.</creatorcontrib><creatorcontrib>Monteiro, Joao Filipe G.</creatorcontrib><creatorcontrib>Berzin, Tyler M.</creatorcontrib><creatorcontrib>Asombang, Akwi W.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Health and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Njei, Basile</au><au>Kanmounye, Ulrick Sidney</au><au>Mohamed, Mouhand F.</au><au>Forjindam, Anim</au><au>Ndemazie, Nkafu Bechem</au><au>Adenusi, Adedeji</au><au>Egboh, Stella-Maris C.</au><au>Chukwudike, Evaristus S.</au><au>Monteiro, Joao Filipe G.</au><au>Berzin, Tyler M.</au><au>Asombang, Akwi W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence for healthcare in Africa: a scientometric analysis</atitle><jtitle>Health and technology</jtitle><stitle>Health Technol</stitle><date>2023-11-01</date><risdate>2023</risdate><volume>13</volume><issue>6</issue><spage>947</spage><epage>955</epage><pages>947-955</pages><issn>2190-7188</issn><eissn>2190-7196</eissn><abstract>Introduction
Artificial intelligence (AI) has greatly transformed healthcare in developed countries. However, there is limited data describing the extent of AI adoption in African healthcare systems. The aim of this study was to understand the state of AI healthcare research in Africa.
Methods
A scientometric analysis was conducted to visualize the state-of-the-art research of AI in healthcare in Africa.
Results
Twenty-six relevant articles, published by 178 authors and affiliated with 96 organizations in 31 countries, were included. The most prolific African countries were South Africa, followed by Nigeria and Ghana. Some articles were published by authors affiliated with non-African countries. None of the contributing authors published more than 2 articles. Only 20 (11.2%) authors collaborated, forming a single cluster. The most common AI tools used in African health systems were deep learning neural networks applied in medical imaging, Adaptive Neuro-Fuzzy Inference Systems, and E-algorithms.
Conclusion
Our results suggest that AI for healthcare in Africa is still in its developmental phase with limited published research. Our social network analysis highlighted a South and West African predominance in the research relational network of AI in healthcare. This discrepancy presents an opportunity for coordination and increased collaboration with healthcare institutions advanced in the use of AI within Africa and beyond.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12553-023-00786-8</doi><tpages>9</tpages></addata></record> |
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| subjects | Adaptive systems Algorithms Artificial intelligence Artificial neural networks Bibliometrics Biological and Medical Physics Biomedical Engineering and Bioengineering Biomedicine Biophysics Citation analysis Citation management software Collaboration Computational Biology/Bioinformatics Electronic health records Engineering Fuzzy logic Fuzzy systems Health care Machine learning Medical imaging Medicine/Public Health Metadata Network analysis Original Paper R & D/Technology Policy Scientometrics Social networks |
| title | Artificial intelligence for healthcare in Africa: a scientometric analysis |
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