Bibliometric Analysis of Artificial Intelligence Research in the Healthcare

Year 2024, Volume: 4 Issue: 1, 13 - 23, 28.04.2024

Emrah Atılgan

Abstract

Artificial intelligence (AI) has a revolutionary impact on the healthcare sector, offering innovative solutions that can lead to significant transformation. Utilizing capabilities such as machine learning, virtual health assistants, natural language processing, robotics, and computer vision, AI technologies enable healthcare professionals to analyze extensive medical data rapidly and accurately. Algorithms driven by AI contribute to early disease diagnosis, risk assessment, and the creation of personalized treatment plans, enhancing the delivery of beneficial solutions to patients and providing more cost-effective healthcare services. In addition to clinical applications, AI shapes healthcare management through patient management, resource allocation, and predictive analytics tools. It is known that AI-supported solutions optimize healthcare services by reducing costs and improving the quality of care. The aim of this article is to elucidate the quantitative and qualitative characteristics of AI in healthcare. Methodologically, a comprehensive bibliometric analysis of academic publications related to AI in healthcare was conducted, presenting information on research and knowledge dissemination at the intersection of this critical technology and the healthcare sector's development. Between 1992 and 2023, 1966 studies indexed in Web of Science, contributed by 7460 authors, were examined. The United States emerged as the leading country in terms of the highest number of studies and citations, with IEEE Access being the leading journal. The most prolific author in this field was Yang Zang, while Diana J. Cook was the most cited author, with the article titled “Ambient intelligence: Technologies, applications, and opportunities,” authored by Diana J. Cook and colleagues, being the most cited article. The most notable topics in this field were “artificial intelligence,” “deep learning,” “machine learning,” and “COVID-19.” The results indicate a significant increase in the use of AI in the healthcare sector in recent years, with this trend expected to continue growing in the coming years. Understanding current trends, major contributors, and evolving aspects of interest provides valuable practical insights for stakeholders aiming to fully leverage the potential of AI in healthcare. Considering that AI is rapidly developing, it can be predicted that its role in health will become more important by contributing to more efficient healthcare systems such as more reliable patient outcomes and increased accessibility. Possible contributions of the obtained results for studies and applications in this field, aspects that need improvement and limitations are discussed in the discussion section.

Keywords

Artificial Intelligence, Healthcare, Deep Learning, Machine Learning

Sağlıkta Yapay Zeka Araştırmalarının Bibliyometrik Analizi

Abstract

Yapay zekâ (YZ), sağlık sektöründe devrim niteliğinde bir etkiye sahip olup, sektörde önemli bir dönüşüme neden olabilecek yenilikçi çözümler sunmaktadır. YZ teknolojilerinden, makine öğrenmesi, sanal sağlık asistanları, doğal dil işleme, robotik ve bilgisayar görüsü gibi imkânların kullanılması, sağlık profesyonellerine geniş kapsamlı tıbbi verileri hızlı ve doğru bir şekilde analiz etme olanağı tanır. YZ tarafından yönlendirilen algoritmalar, hastalıkların erken teşhisine, risk değerlendirmesine ve kişiselleştirilmiş tedavi planlarının oluşturulmasına katkıda bulunarak hastaya sunulacak faydalı çözümleri artırır ve daha ekonomik sağlık hizmetleri sunar. Klinik uygulamaların yanı sıra, YZ, hasta yönetimi, kaynak tahsisi ve öngörüsel analitik araçlarıyla sağlık yönetimini de şekillendirmektedir. YZ tarafından desteklenen çözümlerle sağlık hizmetlerinin optimize edilmesinin maliyetleri düşürdüğü ve bakım kalitesini artırdığı bilinmektedir. Bu makalenin amacı, sağlıkta yapay zekanın nicel ve nitel özelliklerini ortaya koymaktır. Yöntem olarak, sağlıkta YZ ile ilgili akademik yayınlara yönelik kapsamlı bir bibliyometrik analiz yapılmış ve bu kritik teknoloji ile sağlık sektörünün kesişimindeki araştırma ve bilgi yayma alanındaki gelişimle ilgili bilgi sunulmuştur. 1992-2023 yılları arasında, 7460 yazarın katkıda bulunduğu, Web of Science’ta taranan 1966 çalışma incelenmiştir. Bu alanda en çok çalışma üreten ve atıf alan ülke Amerika Birleşik Devletleri, IEEE Access lider dergi olarak bulunmuştur. En çok yayın yapan yazar Yang Zang, en çok atıf alan yazar Diana J. Cook ve en çok atıf alan makale Diana J. Cook ve arkadaşlarının yazdığı “Ambient intelligence: Technologies, applications, and opportunities” başlıklı çalışma olmuştur. Alanda en dikkat çeken konular “yapay zeka,” “derin öğrenme,” “makine öğrenmesi,” ve “COVID-19” olmuştur. Sonuçlar, yapay zekanın sağlık sektöründe kullanımının son yıllarda önemli ölçüde arttığını ve bu trendin önümüzdeki yıllarda da artarak devam etmesinin beklendiğini göstermektedir. Mevcut eğilimlere, başlıca katkıda bulunanlara ve ilgi alanlarının gelişen yönlerine dair bilgi sahibi olmak, sağlıkta YZ'nin potansiyelini tam anlamıyla kullanmayı amaçlayan paydaşlar için değerli pratik bilgiler sunmaktadır. YZ’nin hızla geliştiği dikkate alındığında, sağlıktaki rolünün de daha güvenilir hasta sonuçları, artan erişilebilirlik gibi daha verimli sağlık sistemleri sunulmasına katkı sağlayarak daha önemli hale geleceği öngörülebilir. Elde edilen sonuçların bu alanda yapılacak çalışmalar ve uygulamalar için olası katkıları, geliştirilmesi gereken yanları ve sınırlılıkları tartışma bölümünde ele alınmıştır.

Keywords

Yapay Zeka, Sağlık, Derin Öğrenme, Makine Öğrenmesi

References

• Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.
• Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(310):1–9.
• Tadiboina SN. Benefits of Artificial Intelligence in Education. Webology. 2021;18(5):3779–85.
• Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459–86.
• Al-Aswad LA, Ramachandran R, Schuman JS, et al. Artificial Intelligence for Glaucoma. Ophthalmol Glaucoma. 2022;5(5):e16–25.
• Radanliev P, De Roure D. Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0. Health Technol (Berl). 2023;13(1):11–5.
• Darwiesh A, El-Baz AH, Abualkishik AZ, Elhoseny M. Artificial Intelligence Model for Risk Management in Healthcare Institutions: Towards Sustainable Development. Sustainability. 2022;15(1):420.
• Schork NJ. Artificial Intelligence and Personalized Medicine. Cancer Treat Res. 2019;178:265–83.
• Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging. 2020;4(24):1–22.
• Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Futur Healthc J. 2019;6(2):94–8.
• Tătaru OS, Vartolomei MD, Rassweiler JJ, et al. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives. Diagnostics. 2021;11(2):354
• Loncaric F, Camara O, Piella G, Bijnens B. Integration of artificial intelligence into clinical patient management: focus on cardiac imaging. Rev Española Cardiol. 2021; 74(1):72–80.
• Jadczyk T, Wojakowski W, Tendera M, Henry TD, Egnaczyk G, Shreenivas S. Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology. J Med Internet Res. 2021;23(5):e22959.
• Tursunbayeva A, Renkema M. Artificial intelligence in health‐care: implications for the job design of healthcare professionals. Asia Pacific J Hum Resour. 2023;61(4):845–87.
• Belle A, Thiagarajan R, Soroushmehr SMR, Navidi F, Beard DA, Najarian K. Big Data Analytics in Healthcare. Biomed Res Int. 2015;2015(370194):1–16.
• Kamble SS, Gunasekaran A, Goswami M, Manda J. A systematic perspective on the applications of big data analytics in healthcare management. Int J Healthc Manag. 2019; 12(3):226–40.
• Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 295–336.
• Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res. 2021;133:285–96.
• Donthu N, Kumar S, Pandey N, Lim WM. Research Constituents, Intellectual Structure, and Collaboration Patterns in Journal of International Marketing: An Analytical Retrospective. J Int Mark. 2021;29(2):1–25.
• Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics. 2015;105(3):1809–31.
• Bonilla CA, Merigó JM, Torres-Abad C. Economics in Latin America: a bibliometric analysis. Scientometrics. 2015;105(2):1239–52.
• Van Raan AFJ. Advances in bibliometric analysis: research performance assessment and science mapping. In: Bibliometrics Use and Abuse in the Review of Research Performance. Portland Press Limited; 2014. p. 17–28.
• R Core Team. R: The R Project for Statistical Computing. Vienna, Austria; 2022. Available from: https://www.r-project.org/. Accessed: 12.20.2023
• Aria M, Cuccurullo C. Bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959–75.
• Derviş H. Bibliometric Analysis using Bibliometrix an R Package. J Scientometr Res. 2020;8(3):156–60.
• K-Synth Team. Bibliometrix. Available from: https://www.bibliometrix.org/home/. Accessed: 12.12.2023
• Jain J, Walia N, Singh S, Jain E. Mapping the field of behavioural biases: a literature review using bibliometric analysis. Manag Rev Q. 2022;72(3):823–55.
• Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H. The big picture on Instagram research: Insights from a bibliometric analysis. Telemat Informatics. 2022;73:101876.
• Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21(1):125.
• van Hartskamp M, Consoli S, Verhaegh W, Petkovic M, van de Stolpe A. Artificial Intelligence in Clinical Health Care Applications: Viewpoint. Interact J Med Res. 2019;8(2):e12100.
• Lee D, Yoon SN. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int J Environ Res Public Health. 2021;18(1):271.
• Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. Am J Med. 2019;132(7):795–801.
• Patel VL, Shortliffe EH, Stefanelli M, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46(1):5–17.
• Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med. 2020;7:Article 27.
• Musa IH, Afolabi LO, Zamit I, et al. Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database. Cancer Control. 2022 ;29(1):1–20.
• Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 25–60.
• Schönberger D. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. Int J Law Inf Technol. 2019;27(2):171–203.
• Safdar NM, Banja JD, Meltzer CC. Ethical considerations in artificial intelligence. Eur J Radiol. 2020;122:108768.