Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling

Raman, Raghu; Pattnaik, Debidutta; Hughes, David Laurie; Nedungadi, Prema Article Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Raman, Raghu; Pattnaik, Debidutta; Hughes, David Laurie; Nedungadi, Prema (2024) : Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 9, Iss. 3, pp. 1-18, https://doi.org/10.1016/j.jik.2024.100517 This Version is available at: https://hdl.handle.net/10419/327420 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling Raghu Raman a, *, Debidutta Pattnaik b , Laurie Hughes c , Prema Nedungadi d a Amrita School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala 690525, India b International Management Institute, Bhubaneswar 751 003, India c School of Business and Law, Edith Cowan University, JO 2.331, 270 Joondalup Drive, Joondalup WA 6027, Australia d Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala 690525, India ARTICLE INFO Article History: Received 13 March 2024 Accepted 15 July 2024 Available online 20 July 2024 ABSTRACT In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community’s profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The findings also suggest an opportunity to explore emerging methodologies such as topic modeling and meta-analysis. We dissect the state of the art presented in these reviews, finding themes throughout the broad scholarly discourse through thematic clustering and BERTopic modeling. Categorization of study articles across fields of research indicates dominance in Information and Computing Sciences, followed by Biomedical and Clinical Sciences. Subject categories reveal interconnected clusters across various sectors, notably in healthcare, engineering, business intelligence, and computational technologies. Semantic analysis via BERTopic revealed nineteen clusters mapped to themes such as AI in health innovations, AI for sustainable development, AI and deep learning, AI in education, and ethical considerations. Future research directions are suggested, emphasizing the need for intersectional bias mitigation, holistic health approaches, AI’s role in environmental sustainability, and the ethical deployment of generative AI. © 2024 The Author(s). Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: Thematic analysis Cocitation analysis Topic modeling BERTopic Artificial intelligence Sustainable development goal Cybersecurity Innovation Ethics Blockchain JEL classification: I21 L86 Z00 Introduction The adoption of artificial intelligence (AI) within industry, health, education, and government has profound implications for humans at the societal level (Dwivedi et al., 2021). Researchers have developed a substantial body of literature covering a plethora of AI-related themes across numerous applications, providing valuable insights into the impact of AI systems and applications. This rapidly evolving field has led to a significant number of review articles that distill (Goodell et al., 2021), analyze (Pattnaik et al., 2023), and synthesize (Pattnaik et al., 2024) the vast quantum of knowledge produced. These reviews are crucial for understanding the dynamics of AI applications and their transformative potential. The widespread impact of AI in healthcare, education, agriculture, cybersecurity, and government has generated a considerable volume of academic publications, encompassing diverse genres of AI adoption across multiple sectors (Guo et al., 2020;Hinojo-Lucena et al., 2019). Researchers have endeavored to distill key findings through these review articles, resulting in thematic analyses that highlight essential aspects of AI research. These thematic analyses provide a detailed understanding of how AI applications influence various domains; reveal trends, challenges, and opportunities; and shape future research directions. As the field continues to grow, the synthesis of these diverse insights through a meta-review approach becomes increasingly important. By employing scientometrics and advanced topic modeling techniques such as BERTopic, we can unveil the comprehensive dynamics of AI applications, offering a holistic perspective that guides both current understanding and future scholarly efforts (R. Raman et al., 2024). For instance, extensive research advancements in natural language processing (NLP) systems have been demonstrated by Kreimeyer et al. (2017) and Bannach-Brown et al. (2019), demonstrating the depth of AI applications in language technology. These studies * Corresponding author. E-mail address: [email protected] (R. Raman). https://doi.org/10.1016/j.jik.2024.100517 2444-569X/© 2024 The Author(s). Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Journal of Innovation & Knowledge 9 (2024) 100517 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge highlight how NLP systems have evolved to understand and generate human language with increasing accuracy, enabling applications in areas such as automated translation, sentiment analysis, and conversational agents. Groundbreaking insights into AI applications in medical fields are synthesized by Roy et al. (2019) and Ebrahimighahnavieh et al. (2020), who highlight the potential of AI in neurological research and medical advancements. These reviews reveal how AI technologies can aid in diagnosing neurological disorders, personalizing treatment plans, and improving patient outcomes. Similarly, Liu et al. (2019) and Brinker et al. (2018) analyzed the convergence of AI and healthcare, revealing significant interdisciplinary contributions that span from predictive analytics in patient care to the optimization of hospital operations. In addition to healthcare, the transformative potential of AI in education was explored by Tahiru (2021) and Xu and Ouyang (2022), who illustrated how AI reshapes learning environments. Their studies discuss the integration of AI in personalized learning systems, intelligent tutoring, and automated grading, which enhance educational experiences and outcomes. The application of AI has also extended to sustainable development, with intensive use of renewable energy and agriculture, as discussed by Mosavi et al. (2018) and Carvalho et al. (2019). These works demonstrate how AI-driven solutions, such as precision farming and energy management systems, contribute to sustainability by optimizing resource use and reducing environmental impact. Conversely, the role of AI in software quality assurance is detailed by Li et al. (2020) and Meiliana et al. (2017), who delve into software defect prediction and the complexities of software development. Their reviews highlight how AI techniques improve software reliability and efficiency by predicting and mitigating potential defects during the development lifecycle. Furthermore, contributions to intelligent transport systems are underscored by Sirohi et al. (2020) and Noaeen et al. (2022), who emphasize AI’s potential to enhance traffic safety. These studies explore how AI technologies, such as autonomous vehicles and smart traffic management systems, reduce accidents and improve traffic flow. Moreover, the modern applicability of AI in urban administration and planning is illustrated by Di Vaio et al. (2020) and Darko et al. (2020), who elucidate its multifaceted impacts on smart cities. Their analyses revealed how AI can optimize urban services, enhance public safety, and improve the quality of life for city inhabitants through intelligent infrastructure and data-driven decision-making. Moreover, the intersection of AI with blockchain technology was described by Kumar et al. (2023) and Ekramifard et al. (2020), revealing the synergies between these revolutionary technologies. Their studies discuss how AI enhances blockchain capabilities in secure data transactions and decentralized applications, while blockchain provides robust frameworks for AI data integrity and provenance. AIpowered emotional intelligence was explored in “deep learning for emotion analysis”by Bouwmans et al. (2018) and Canedo and Neves (2019). These reviews delve into the nuances of emotion identification and its applications in areas such as mental health monitoring, customer service, and human-computer interaction. Additionally, deep learning applications in recommendations and molecular science are discussed by Portugal et al. (2018),Murad et al. (2018),Martinelli (2022), and Wu et al. (2022). Their findings highlight how deep learning algorithms enhance recommendation systems by personalizing content delivery and accelerating discoveries in molecular science through predictive modeling of molecular interactions. The role of AI in edge computing and cybersecurity was analyzed by Martins et al. (2020) and Manzoor et al. (2019). These studies show how AI improves the efficiency and security of distributed computing systems by enabling real-time data processing and threat detection at the edge of the network. The ethical dimensions of AI in healthcare are highlighted by Milne-Ives et al. (2020) and Loh et al. (2022), who provide invaluable insights into the moral issues surrounding AI-driven healthcare applications. They discuss concerns such as patient privacy, algorithmic bias, and the need for transparent and accountable AI systems. Furthermore, intelligent diagnostics were examined by Christodoulou et al. (2019) and Fleuren et al. (2020), who explored the synthesis of machine learning models for diagnostic accuracy. Finally, the challenge of mitigating bias in AI decision-making was explored by Sun et al. (2019) and Pagano et al. (2023), who addressed critical issues of fairness and accountability in AI systems by proposing strategies for identifying and reducing biases in AI algorithms. Despite the extensive coverage of AI applications through individual review articles, there remains a significant opportunity to synthesize these diverse insights into a comprehensive overview. The concept of a “review of reviews”or meta-review has been effectively employed in other disciplines to consolidate findings, identify research gaps, and propose new directions. For example, Schryen and Sperling (2023) conducted a meta-review of operations research, highlighting predominant trends and underexplored areas in 709 reviews published between 2011 and 2020. Their analysis revealed a focus on scoping and selective reviews, emphasizing the importance of systematically organizing and synthesizing existing knowledge. Similarly, Moro et al. (2023) provided an umbrella review of productservice systems, offering a panoramic overview that identified wellresearched areas and those still requiring further exploration. In another instance, Sadeghi-Niaraki (2023) analyzed contemporary IoT reviews, pinpointing critical challenges and opportunities within the field. Furthermore, Risso et al. (2023) employed a systematic literature review to extend discussions in 103 review papers on blockchain technology in supply chain management, providing multifaceted insights and identifying future research directions. The high engagement and success of meta-reviews in various fields underscore the value of this research approach. However, a similar comprehensive study of AI remains largely untapped, presenting an opportunity to synthesize and generate novel findings from the extensive body of AI research. The significant corpus of AI publications and reviews exploring its various applications suggests that synthesizing semantic clusters from collective perspectives on AI could effectively direct future scholarly efforts (Pattnaik et al., 2024). This research aims to fill this existing gap by offering novel insights into AI through thematic clustering of subject categories and employing the BERTopic modeling approach (Grootendorst, 2022). By leveraging these advanced techniques, we can unveil the comprehensive dynamics of AI applications, providing a holistic perspective that enhances current understanding and guides future research and development in this rapidly evolving field. We propose the following research questions: RQ1: What are the key domains and applications currently being transformed by AI, and how is this transformation characterized? RQ2: Can the proposed modeling technique develop novel insight into the global transformative impact of AI across diverse domains? RQ3: What are the potential future directions and innovations? By addressing these research questions, this study unveils the dynamics of AI applications through a comprehensive review of reviews employing scientometrics and advanced bibliometric modeling. Our investigation reveals the transformative impact of AI across key domains, including healthcare, engineering, environmental sustainability, business, and human-computer interaction. Specifically, AI advancements in healthcare have revolutionized diagnostics and personalized medicine, while its contributions to engineering and environmental applications have promoted sustainability and smart infrastructure. In business, AI enhances decision support systems and operational efficiency, and its influence on digital infrastructure improves human-computer interactions. Additionally, our topic modeling analysis provides novel insights into the broad applicability of AI, highlighting its role in deep learning technologies, education, industry, blockchain, cybersecurity, and ethical considerations. These findings not only answer critical research questions but also identify R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 2 future directions and innovations, setting a new benchmark for literature reviews in rapidly evolving scientific domains. In the remaining sections of the study, Section 2 discusses the study methods, detailing the systematic approach and tools used for data collection and analysis. Section 3 highlights the key results, presenting the major findings from our scientometric and topic modeling analyses. In Section 4, we offer a thorough discussion of the results, emphasizing the implications of our findings for both practice and theory and suggesting potential avenues for future research. Finally, in Section 5, we conclude the work by summarizing the study’s contributions and reflecting on its significance in advancing the understanding of AI applications. Methods The proposed research methods offer a unique contribution to the analysis of artificial intelligence (AI) research by combining the systematic rigor of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for data collection, the interdisciplinary insight of All Science Journals Classification (AJSC) subject categories for thematic clustering, and the advanced semantic analysis capabilities of BERTopic modeling. As illustrated in Fig. 1, this methodology ensures a comprehensive and bias-minimized dataset, uncovers cross-disciplinary thematic clusters through a structured classification system, and extracts nuanced emerging research themes by leveraging state-of-the-art natural language processing techniques. Together, these elements constitute a novel approach that enhances the depth, accuracy, and relevance of AI research analysis, setting a new benchmark for conducting literature reviews in rapidly evolving scientific domains. PRISMA To ensure a systematic and transparent approach in conducting this meta-review, we adhered to the PRISMA guidelines (Moher et al., 2009). The PRISMA protocol provides a structured framework for identifying, selecting, and critically appraising relevant studies, as well as for collecting and analyzing data from these studies. This protocol enhances the rigor and reproducibility of systematic reviews by providing a standardized reporting methodology. Following these guidelines, we systematically collected articles from the Scopus database on 23rd January 2024 (Page et al., 2021;Rama et al., 2023; Raman et al., 2022). Scopus was chosen due to its comprehensive coverage and high-quality indexing of peer-reviewed literature. The study period spans from 2014 to 2023, with the search terms “((artificial intelligence”OR “AI”OR “machine learning”OR “deep learning”OR “neural network*”OR “supervised learning”OR “unsupervised learning” OR “reinforcement learning”OR “natural language processing”OR “NLP” OR “computer vision”OR “cognitive computing”) AND (biblio* OR scientome* OR “literature review”OR “systematic review”). The document types included were articles, conference papers, reviews, and book chapters, leading to a final dataset of 3767 articles for analysis. Performance analysis In our scientometric analysis, we used a comprehensive set of metrics to scrutinize the performance, collaboration dynamics, and impact of scholarly publications, as reported in previous reviews (Kokol et al., 2021;Pattnaik et al., 2021,2023,2024). The approach undertaken for the design of this research focuses on a range of indicators to fully explore reviews on the applications of AI. The metric total reviews (TRs) showcased our overall research. By distinguishing reviews that are solo-authored (SA) and coauthored (CA), we identify the collaboration patterns that are crucial for knowledge dissemination (Baker et al., 2020). While CA places more emphasis on teamwork, SA reflects individual contributions. The level of collaboration evident in the former reviews is further investigated by analyzing the number of contributing authors (NCA), which captures the range of networks and academic involvement. Furthermore, the average number of authors per coauthored article (AACA), collaboration index (CI), and collaboration coefficient (CC) provide nuanced insights into the shifting intensity and patterns of collaboration over time (Donthu et al., 2021). In addition to reviewing the number of articles, we analyzed citations to assess the impact of review articles. The number of cited reviews (NCR) indicates the number of review articles frequently referred to, while total citations (TCs) provide an overview of the overall impact. To standardize for variations in publication age, we calculate average citations per cited review (TC/CR), which offers an adjusted impact measure. Conversely, various indices, including the h-index, g-index, and i-index, are utilized to deepen our understanding of citation influence. The h-index focuses on highly cited reviews, representing the number of reviews with at least h citations. The gindex emphasizes productivity by indicating the number of top-cited reviews with at least g2 citations. Simultaneously, the i-indices (i-10, i-100, and i-200) reveal the number of reviews cited at least 10, 100, and 200 times, respectively. Additional metrics such as the number of active years (NAY) reveal the duration of review publications in AI applications. Combining this with productivity per active year (PAY) gives us a better sense of sustained scholarly output over time. Thematic clustering The All Science Journals Classification (ASJC) subject categories, a classification system used by Scopus for indexing source titles within a structured hierarchy spanning various disciplines and subdisciplines, serves as the framework for cross-disciplinary thematic analysis (Haddawy et al., 2017;Hassan et al., 2017). We employed VOSviewer, a software application crafted for constructing and visualizing bibliographic networks (van Eck & Waltman, 2010). The basis of our analysis lies in the co-occurrence of ASJC subject categories for each publication. Each node represents a distinct ASJC publication, with lines connecting nodes indicating the frequency of co-occurrence. The color of a node often denotes the cluster or group to which a publication belongs, and each color signifies a different thematic cluster (Goodell et al., 2021;R. Raman et al., 2024). This clustering is grounded in the similarity of co-occurrence patterns, implying frequent discussion together in the literature. The distance between nodes in the visualization is also significant; a shorter distance indicates a stronger or more frequent co-occurrence, suggesting closer relationships or a higher degree of topic relevance. Topic modeling Although a number of topic modeling approaches have been utilized within the literature, studies that have developed an empirical analysis of various approaches, such as nonnegative matrix factorization (NMF), To2Vec, and latent Dirichlet allocation (LDA), have identified BERTopic as “being able to generate novel insights using its embedding approach”(Egger & Yu, 2022). At its core, BERTopic is a modeling technique that leverages the powerful contextual embeddings within BERT and the class-based term frequency-inverse document frequency (c-TF-IDF) algorithm to compare the importance of terms within a dense cluster and develop term representation (S anchezFranco & ReyMoreno 2022). Postdata extraction, a thorough preprocessing step involving text-cleaning procedures, NLP techniques, and tokenization, enhanced the quality and uniformity of the dataset. The utilization of sentence embeddings generated using the “all-mpnet-base-v2”model from the Sentence Transformer and dimensionality reduction using uniform manifold approximation and projection (UMAP) further generated the dataset for meaningful topic extraction and visualization (McInnes et al., 2020). The BERTopic R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 3 Fig. 1. Research framework. R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 4 model was fitted to the preprocessed text content, extracting distinct topics and corresponding probabilities for each article within a topic. The generated topics were scrutinized for coherence, and the distribution of articles across topics was examined, providing insights into the degree of association between articles and identified topics. This comprehensive and advanced approach in our topic modeling analysis ensures the reliability and robustness of our study findings. Results Performance analysis As shown in Table 1, our study thoroughly examined the 3767 reviews that were cited 63,577 times. Such statistics underscore the extensive scholarly engagement and influence within the specialized field of research. The high h-index of 109 and g-index of 176 suggest a substantial impact, emphasizing the significance and relevance of the former reviews. Moving to the coauthorship insights in Panel B, our study unveils a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers engaged in synthesizing state-of-theart research on AI applications. A collaboration index of 3.83 indicates a considerable degree of teamwork, fostering a rich environment for knowledge exchange created and disseminated through reviews. Additionally, the data illustrate that the average number of authors per coauthored article is 5, emphasizing the collective effort in producing the reviewed content. This collaborative spirit likely contributes to the field’s diversity and depth of perspectives. In Panel C, the paper categorizes the types of reviews on AI applications, revealing a predominant focus on systematic reviews and bibliometric analyses, with 2707 and 651 instances, respectively. This signifies a methodological inclination toward the comprehensive synthesis of literature. Moreover, including other review types, such as topic modeling and meta-analysis, showcases the methodological diversity in understanding the dynamics of AI applications. Overall, the inferential summary highlights a multidimensional exploration of the literature, encompassing collaboration patterns, impact metrics, and methodological approaches within the AI domain. The findings in Table 1 are further extended in Fig. 2, which maps the evolution of the various forms of reviews between 2014 and 2023. The substantial increase in systematic reviews (SLRs) over the years, reaching 1061 in 2023, reinforces our previous finding of a sustained interest in comprehensive literature synthesis. Scientometric reviews also gained prominence, reaching 281, indicating a growing focus on quantitative analysis within the domain. Building on these insights, we delve more deeply into specific aspects of AI research in the subsequent subsections. We explore the fields of research analysis to identify key areas of focus and emerging trends. This will be followed by an examination of thematic clusters, providing a detailed discussion on the major themes and topics that have shaped the AI research landscape. Additionally, we analyze BERT-enabled topics to uncover nuanced patterns and insights derived from advanced natural language processing techniques. Together, these discussions aim to provide a holistic understanding of the current state and future directions of AI research. Fields of research analysis The Scopus database further uses the Australian and New Zealand Standard Classification of Occupations (ANZSCO, 2013) to categorize publications into fields of research (FoRs). Fig. 3 categorizes the study articles under the field of research (FoR). Most notably, information and computing sciences dominate the landscape regarding total reviews and citations, underscoring their pivotal role in the synthesis of AI research. This trend is not surprising given the technical nature of AI. Therefore, biomedical and clinical sciences and health sciences have presented a significant volume of AI reviews, which indicates the growing importance of AI applications in these fields. The high citation counts in these areas suggest that the reviews generated are prolific and impactful, influencing further studies and developments. Although having fewer publications in comparison, other fields, such as engineering, commerce, management, tourism, services, and education, still have a notable presence, reflecting AI technologies’interdisciplinary and wide-reaching impact. Fields such as built environment and design, biological sciences, psychology, mathematical sciences, and human society, while contributing less in number, reveal the diverse application of AI across a broad spectrum of research areas. This diversity in application areas highlights the versatility and broad applicability of AI technologies in various aspects of scientific and technological research. Thematic clusters The FoRs based on the ANZSRC classification system reveal the distribution of AI across broad academic and research disciplines. In contrast, subject categories based on the ASJC system revealed more granular interconnections via four distinct clusters within AI research (Fig. 4). Cluster 1 (red): AI in healthcare and life sciences informatics This cluster is characterized by the integration of AI with healthcare and life sciences to enhance data management, diagnostics, and treatment protocols. Key terms such as “health informatics”and “health information management”suggest the application of AI in organizing and analyzing health data. “Biomedical engineering”and “bioengineering”point to the design of AI-driven medical devices Table 1 Overview. Panel A. Descriptive statistics Total reviews (TR) 3767 Number of cited reviews (NCR) 2748 Total citations (TC) 63,577 Average citations (TC/TR) 23 h-index 109 g-index 176 i-10 1190 i-100 129 i-250 28 i-500 9 Number of active years (NAY) 10 Productivity per active year (PAY) 377 Panel B. Coauthorship information Number of contributing authors (NCA) 18,189 Number of affiliated authors (excludes repetitions) (NAA) 15,677 Authors of single-authored documents (ASA) 121 Authors of coauthored documents (ACA) 15,568 Single-authored documents (SA) 124 Coauthored documents (CA) 3643 Collaboration index (CI) 3.83 Collaboration coefficient (CC) 0.79 Average authors per coauthored article 5 Panel C. Type of review Systematic review/systematic literature review (SLR) other than Scientometrics/Bibliometrics 2707 Scientometric/Bibliometric review 651 Other form of review 409 Topic model 48 SLR and topic model 19 Scientometric/Bibliometric and topic model 20 Other form of review and topic model 9 Meta-analysis 4 SLR and meta-analysis 4 Scientometric/Bibliometric and meta-analysis − Other form of review and meta-analysis − Note: This table presents an overview of the study articles. Citations reported were confined to the search date. R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 5 and systems. With “neurology”and “oncology,”there is an implication of AI in specialized medical research and treatment planning, possibly using ML techniques for pattern recognition in disease diagnosis. “Cognitive neuroscience”and “psychiatry and mental health” indicate the exploration of AI in understanding and treating neurological and mental health conditions. This cluster also likely includes the use of AI for genomic sequencing and personalized medicine, as suggested by “molecular biology”and “biochemistry.”Table 2 shows some of the notable works constituting the cluster. Significant contributions to the field of AI in healthcare were made through a systematic review by Xiaoet al. (2018), who focused on deep learning applications in electronic health record (EHR) data. Their research, conducted between 2010 and 2018, involved a thorough analysis of 98 articles focusing on the use of deep learning in Fig. 2. Temporal evolution of the types of reviews on AI applications. Fig. 3. Classification of AI reviews based on FoRs (ANZSRC 2020 code). R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 6 healthcare informatics. This study is pivotal for understanding how deep learning architectures can be effectively applied to various types of health data, addressing critical tasks such as disease detection and the prediction of clinical events. The authors noted deep learning’s superiority in handling raw data, which aligns with the ongoing shift toward more data-driven approaches in healthcare informatics. However, the paper also delves into the challenges inherent in this field, such as the need for improved data quality and the complexities of model interpretability in a healthcare context. These issues are crucial considering the sensitive nature of health data and the need for reliable and understandable AI systems in medical settings. Moreover, the discussion on the difficulties in integrating deep learning models with existing EHR systems reflects a significant challenge in the broader theme of AI in healthcare informatics. Contreras and Vehi (2018) explored the integration of AI with modern technologies such as medical devices, mobile computing, and sensors to enhance diabetes management, a critical issue in the cluster theme of AI in healthcare and life sciences informatics. Their comprehensive review, which analyzed 141 articles from 2010 to 2018, focused on using AI to manage diabetes and its complications. This paper highlights the development of AI-driven tools for prediction and prevention in diabetes care, emphasizing how these advancements can improve patient quality of life. Their findings reveal a significant shift toward data-driven methods in diabetes management, underscoring the potential of AI in tailoring treatment to individual needs and in leveraging large datasets for improved management strategies. They also noted the growing research in closed-loop systems and blood glucose (BG) prediction models, reflecting the dynamic evolution of AI applications in this field. They emphasized the importance of continuing research in AI for diabetes management, particularly in enhancing the safety of automated pancreas (AP) systems and open-loop tools. The paper also addresses the ethical considerations of using AI in healthcare, including the risks associated with personal data release and the potential for discrimination. A systematic review conducted by Yassin et al. (2018) focused on the analysis of computer-aided diagnosis/detection (CAD) systems applicable to breast cancer. Their study, which analyzed 154 selected academic articles, delved into the current state and advancements of CAD systems, especially in their application to breast cancer detection. This paper highlights the increasing reliance on machine learning technologies, such as SVM classifiers, for breast tissue classification and notes the effectiveness of these AI methods in supporting medical experts. This review also discusses the practical challenges and considerations in implementing CAD systems in clinical settings, including issues related to false positives, costs, and the Fig. 4. Clustering of cross-disciplinary subjects of AI research. Table 2 Highly cited articles representing cluster 1. Total Citations Author(s) Title Subject Category (ASJC) 380 Xiao et al. (2018) “Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review” Health Informatics 257 Contreras and Vehi (2018) “AI for diabetes management and decision support: Literature review” Health Informatics 239 Yassin et al.(2018) “Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review” Computer Science Applications| Software| Health Informatics R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 7 necessity for proper training. The authors advocate for the integration of CAD systems into clinical practice, emphasizing that for widespread adoption, CAD systems must be time-efficient, cost-effective, and demonstrably improve physician performance. In the future, the authors recommend the development of standardized public image databases that include diverse modalities and even genetic data to enhance the accuracy and reliability of CAD systems. They also identified deep learning and swarm intelligence as promising areas for future research in CAD system development. This paper concludes by underscoring the importance of incorporating multiple imaging modalities and advanced technologies such as 3D mammography to improve the efficiency and efficacy of CAD systems in breast cancer detection. Cluster 2 (green): AI in engineering systems and sustainable technologies AI is seen as a catalyst for innovation across various engineering fields. “Computer science applications”and “information systems” refer to AI’s role in optimizing system operations and data processing. The terms “electrical and electronic engineering”and “instrumentation”suggest developing smart sensors and controls that leverage AI for improved efficiency and automation. “Renewable energy, sustainability, and the environment,”alongside “energy engineering and power technology,”likely involve AI in smart grids, energy consumption prediction, and the optimization of renewable energy sources. The contribution of AI to “safety, risk, reliability and quality”implies the use of predictive analytics and ML for risk assessment and quality control in engineering projects. This cluster may also encompass the development of AI tools for environmental monitoring and sustainable urban planning, as indicated by “geography, planning, and development.”Table 3 presents some of the notable works representing the cluster. A systematic review by Patrício & Rieder (2018) highlighted the intersection of AI in engineering systems and sustainable technology, specifically focusing on the use of computer vision and AI in precision agriculture. Their study, centering on the five most produced grains globally (maize, rice, wheat, soybean, and barley), analyzed 25 papers from the past five years. This review showcased various applications of computer vision in agriculture, such as disease detection, grain quality assessment, and phenotyping. They emphasized the potential of leveraging GPUs and advanced AI techniques such as deep belief networks for enhancing computer vision methods in agriculture. Additionally, the study identified gaps in the development of intelligent devices that integrate computer vision with agricultural machinery and drones. The authors suggested that the expansion of GPUs and AI could benefit the classification of gluten-containing grains such as wheat, oats, and barley. Similarly, Sharma et al. (2020) conducted a study crucial to the theme of AI in engineering systems and sustainable technology, particularly focusing on the use of ML in agricultural supply chains (ASCs). Their systematic review, which encompassed 93 research papers, explored the diverse applications of ML algorithms across various phases of ASCs. This study emphasized the role of ML in enhancing agricultural sustainability by addressing key challenges such as productivity, water conservation, and soil health. A significant contribution of this work is the development of an ML application framework for sustainable ASCs designed to guide real-time, datadriven decision-making in ASCs. This framework aims to provide actionable insights for researchers, practitioners, and policymakers to manage ASCs effectively, thereby improving agricultural productivity and sustainability. This review underscores the vast potential of ML in ASCs, highlighting its effectiveness in making predictive classifications and improving the overall efficiency of ASC operations. The authors also discussed how ML-driven technologies could enhance farm productivity and profitability through the analysis of data from sensors and drones. Furthermore, they noted that integrating ML data with other technologies such as blockchain could improve supply chain visibility, transparency, and traceability. However, the study also identified areas needing further investigation, such as the comprehensive management of data across ASC phases and the measurable impact of ML on ASC visibility. Furthermore, Mosavi et al. (2019) investigated the role of ML in engineering systems and sustainable technology, specifically in energy system modeling, design, and prediction. Their paper presents an extensive review and a novel taxonomy of ML models used in energy systems. This study identifies and classifies ML models based on technique, energy type, and application area, providing a comprehensive assessment of their performance and discussing challenges and future research opportunities. A key finding of their research is the remarkable improvement in the accuracy, robustness, precision, and generalization abilities of ML models, especially through hybridization. These hybrid ML models have shown significant effectiveness in renewable energy system applications, such as solar and wind energy, contributing to energy efficiency, governance, and sustainability. The study also highlights the integration of ML with smart sensors, smart grids, and IoT technologies, facilitating the use of big data for informed decision-making and enhancing model efficiency. The paper concludes that novel hybrid ML models outperform conventional models, suggesting a continuing trend toward more advanced hybrid models for sophisticated energy system applications. This emphasis on hybrid models aligns with the increasing need for accurate and efficient renewable energy systems, considering their environmental dependency and the challenges in grid management and power generation forecasting. Cluster 3 (blue): AI-enhanced business intelligence and strategic management The focus of this cluster is on the application of AI to improve business intelligence, strategic decision-making, and operational efficiency. “Information systems and management”and “general computer science”suggest using AI for data-driven decision support systems. With “strategy and management”and “industrial and manufacturing engineering,”there is an indication of AI for optimizing manufacturing processes and strategic business planning. “Management of technology and innovation”and “management information systems”emphasize AI’s role in managing technological advancements and integrating AI into corporate information infrastructures. Keywords such as “decision sciences”and “modeling and simulation”imply the use of AI for predictive modeling and simulation in business scenarios. This cluster also suggests AI applications in finance and economics, as denoted by “economics, econometrics, Table 3 Highly cited articles representing cluster 2. Total Citations Author(s) Title Subject Category (ASJC) 508 Patrício and Rieder (2018) “Computer vision and AI in precision agriculture for grain crops: A systematic review” Agronomy and Crop Science| Forestry| Horticulture| Computer Science Applications 312 Sharma et al.(2020) “A systematic literature review on machine learning applications for sustainable agriculture supply chain performance” Computer Science| Management Science and Operations Research| Modeling and Simulation 289 Mosavi et al.(2019) “State of the art of machine learning models in energy systems, a systematic review” Energy | Fuel Technology| Renewable Energy, Sustainability and the Environment| | Control and Optimization R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 8 the coverage and emphasis present in Scopus, potentially limiting the comprehensiveness and diversity of perspectives in the broader landscape of AI research. Conclusions In conclusion, this study provides a comprehensive synthesis of AI research by employing systematic review methodologies, thematic clustering, advanced topic modeling techniques, and content analysis. Our investigation revealed the transformative influence of AI across key domains, such as healthcare, engineering, environmental applications, business, and technology, characterized by significant advancements in diagnostics, personalized medicine, sustainability, smart infrastructure, decision support systems, and digital experiences. Specifically, AI in healthcare and life sciences has revolutionized diagnostics and personalized medicine (Cluster 1), while its role in engineering and environmental applications underscores its contributions to sustainability and smart infrastructure (Cluster 2). In business and management, AI enhances decision support systems and operational optimization (Cluster 3), and its influence on digital infrastructure and human-computer interfaces highlights its broad applicability (Cluster 4). Our findings demonstrate AI’s paradigm-shifting potential in healthcare, advancing intelligent diagnostics and personalized medicine, and sustainable development, contributing to environmental sustainability and smart city initiatives (RQ2). The exploration of deep learning technologies showcases AI’s impact on emotion analysis, molecular science, and natural language processing (NLP) systems. AI-driven innovations extend to education and industry, optimizing supply chains and software development, while integration with blockchain and cybersecurity enhances data security and privacy. Ethical considerations and bias mitigation emphasize the necessity of responsible AI practices. By synthesizing diverse insights and employing advanced modeling techniques, we provide a holistic perspective that guides both current understanding and future scholarly efforts (RQ3). This research not only answers key research questions but also sets a new benchmark for conducting literature reviews in rapidly evolving scientific domains. The integration of scientometrics and BERTopic modeling offers a novel approach to unveiling the dynamics of AI applications, ensuring a deeper, more accurate understanding of the transformative impact of AI across multiple sectors and paving the way for future advancements in this critical field. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Raghu Raman: Writing −review & editing, Writing −original draft, Supervision, Methodology, Data curation, Conceptualization. Debidutta Pattnaik: Writing −review & editing, Writing −original draft, Methodology, Data curation. Laurie Hughes: Writing −review & editing, Writing −original draft. Prema Nedungadi: Writing − review & editing, Writing −original draft. Data availability statement Data associated with our study is available as supplementary file Funding Statement This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Supplementary materials Supplementary material associated with this article can be found in the online version at doi:10.1016/j.jik.2024.100517. Appendix Topics, keywords and representative type of articles on each topic Topic Keyterms and their Probability Representative Articles APY Advances in NLP Systems natural language (0.33), natural language processing (0.32), processing nlp (0.23), language processing nlp (0.23), text mining (0.19), nlp methods (0.17), nlp systems (0.15), review natural language (0.15), nlp applications (0.15), language processing systematic (0.15) Kreimeyer et al. (2017);Bannach-Brown et al. (2019); Patra et al. (2021); Wahdan et al. (2020); Khanbhai et al. (2021); Tsou et al. (2020); Mellia et al. (2021); Turchioe et al. (2022); Baviskar et al. (2021); Samant et al. (2022) 2021.4 AI in Brain Health Analysis alzheimer disease (0.32), autism spectrum (0.24), mild cognitive impairment (0.21), eeg signals (0.19), neuroimaging data (0.17), brain computer (0.16), neurodegenerative disorder (0.16), parkinson disease pd (0.16), alzheimer disease systematic (0.15), epileptic seizure (0.15) Roy et al. (2019);Ebrahimighahnavieh et al. (2020); Mostafa et al. (2019); de Belen et al. (2020); Grueso & Viejo-Sobera (2021); Loh et al. (2020); Saeidi et al. (2021); Alzahab et al. (2021); Tzimourta et al. (2021); Maitín et al. (2020) 2021.7 AI in Cancer Diagnosis deep learning (0.20), breast cancer (0.19), medical imaging (0.15), review meta analysis (0.15), prostate cancer (0.14), neural network (0.14), convolutional neural network (0.13), intelligence ai (0.13), chest ray (0.13), cancer detection (0.13) Liu et al. (2019);Brinker et al. (2018); Huang et al. (2020); Soffer et al. (2020); Kassem et al. (2021); Harris et al. (2019); Abreu et al. (2016); Ghaderzadeh & Asadi (2021); Zhou et al. (2021); Mahmood et al. (2020) 2022.0 AI in Education ai education (0.24), artificial intelligence education (0.23), intelligence education (0.23), educational data (0.19), education artificial (0.18), education artificial intelligence (0.18), education systematic literature (0.17), educational data mining (0.17), student dropout (0.17), online higher education (0.16) Tahiru (2021);Xu and Ouyang (2022); Sekeroglu et al. (2021); Okewu et al. (2021); Kaddoura et al. (2022); Salas-Pilco et al. (2022); Baashar et al. (2021); Xu and Ouyang (2022); Issah et al. (2023); Ramírez Luelmo et al. (2021) 2022.1 AI in Energy and Agriculture deep learning (0.19), computer vision (0.17), systematic literature review (0.16), neural network (0.16), remote sensing (0.16), renewable energy (0.15), neural networks (0.14), crop yield prediction (0.14), food image (0.13), precision agriculture (0.13) Mosavi et al. (2018);Carvalho et al. (2019); van Klompenburg et al. (2020); Mosavi et al. (2019);W € aldchen & M€ ader (2018); Flah et al. (2021); Sony et al. (2021); Moayedi et al. (2020); Leukel et al. (2021); Singh et al. (2021) 2021.9 AI in Software Defect Prediction defect prediction (0.35), software defect (0.33), software defect prediction (0.32), software testing (0.28), software development (0.28), learning software (0.27), software quality (0.26), machine learning software (0.26), software fault prediction (0.23), code smell (0.22) Li et al. (2020);Meiliana et al. (2017); Jorayeva et al. (2022); Saharudin et al. (2020); Matloob et al. (2021); Stradowski & Madeyski (2023); Habtemariam et al. (2022); Kaur et al. (2020); Brown et al. (2022); Sathyaraj & Prabu (2016) 2020.8 (continued) R. Raman, D. Pattnaik, L. Hughes et al. Journal of Innovation & Knowledge 9 (2024) 100517 15 References ANZSCO, A. (2013). Australian and New Zealand Standard Classification of Occupations, Version 1.2. Canberra: Australian Bureau of Statistics. Baker, H. K., Kumar, S., & Pattnaik, D. 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(Continued) Topic Keyterms and their Probability Representative Articles APY AI in Traffic Safety trafficflow (0.27), traffic congestion (0.25), trajectory prediction (0.23), transportation systems (0.22), safety critical systems (0.22), path planning (0.21), road safety (0.20), trafficflow prediction (0.20), intelligent transport (0.20), public transportation (0.20) Sirohi et al. (2020);Noaeen et al. (2022); Nascimento et al. (2020); Sun et al. (2021); Al-Masrur Khan et al. (2020); Ali & Mahmood (2018); Deshmukh (2018); Behrooz & Hayeri (2022); Di Felice et al. (2019); Mannan et al. (2023) 2021.7 AI Integration in Smart Cities literature review (0.22), smart cities (0.21), intelligence ai (0.20), human resource management (0.19), knowledge management (0.17), ai adoption (0.16), research ai (0.15), ai marketing (0.15), adoption artificial intelligence (0.14), ai public (0.14) Di Vaio et al. (2020);Darko et al. (2020); Vrontis et al. (2022); Yigitcanlar et al. (2020); Sousa et al. (2019); Mustak et al. (2021); Peres et al. (2020); Zuiderwijk et al. (2021); Mariani et al. (2022); Ribeiro et al. (2021) 2022.0 AI Research Landscape bibliometric analysis (0.29), artificial intelligence bibliometric (0.19), intelligence bibliometric (0.19), intelligence bibliometric analysis (0.17), artificial intelligence research (0.16), intelligence research (0.16), artificial intelligence ai (0.14), research topics (0.14), latent dirichlet allocation (0.14), ai research (0.13) Guo et al. (2020);Hinojo-Lucena et al. (2019); De Felice & Polimeni (2020); Jha et al. (2017); Shukla et al. (2019); Hwang & Tu (2021); Song & Wang (2020); Shen et al. (2022); Belmonte et al. (2020); Zhang et al. (2022) 2021.4 AI-Driven Blockchain blockchain technology (0.50), ai blockchain (0.42), intelligence blockchain (0.38), artificial intelligence blockchain (0.38), blockchain artificial intelligence (0.35), blockchain artificial (0.35), ai enabled blockchain (0.27), blockchain machine learning (0.25), intelligence ai blockchain (0.25), ai blockchain technology (0.23) Kumar et al. (2023);Ekramifard et al. (2020); Karger (2020); Morriello (2019); Vincent et al. (2023); Hajizadeh et al. (2023); Sharma et al. (2023); Chen et al. (2023); de Bem Machado et al. (2023); Abidemi et al. (2023) 2022.3 Deep Learning for Emotion Analysis face recognition (0.41), expression recognition (0.32), facial expression recognition (0.31), object detection (0.28), face liveness detection (0.28), behavior detection (0.27), face expression (0.25), human emotion recognition (0.25), human activity recognition (0.24), deep learning face (0.23) Bouwmans et al. (2019); Canedo and Neves (2019); Zhang et al. (2021); Ullah et al. (2021); Chrysler et al. (2021); Hassen et al. (2022); Khairnar et al. (2023); Pangestu et al. (2022); C^ ırneanu et al. (2023); Kaur & Singh (2023) 2021.6 Deep Learning for Recommendations recommender systems (0.62), recommendation systems (0.49), learning recommendation (0.37), based recommendation systems (0.34), learning recommender (0.33), deep learning based (0.32), deep learning recommender (0.32), learning based recommendation (0.29), recommender systems systematic (0.29), recommendation social (0.27) Portugal et al. (2018); Murad et al. (2019); Den Hengst et al. (2020); Brunialti et al. (2015); Necula & P av aloaia (2023); Lalitha & Sreeja (2021); Selma et al. (2021); Torkashvand et al. (2023); Krishnamoorthi & Shyam (2023); Li et al. (2023) 2020.7 Deep Learning in Molecular Science drug discovery (0.41), molecular similarity (0.35), molecular similarity searching (0.32), drug design (0.29), protein function prediction (0.28), systems biology (0.27), deep learning drug (0.26), learning drug (0.26), protein science (0.25), anticancer drug response (0.25) Martinelli (2022);Wu et al. (2022); Koutroumpa et al. (2023); Villalobos-Alva et al. (2022); Procopio et al. (2023); Nasser et al. (2023); Oguike et al. (2022); Yan et al. (2023); Faiz et al. (2023); Praveena et al. (2023) 2022.0 Edge Computing and Cybersecurity intrusion detection (0.25), internet things (0.24), internet things iot (0.19), things iot (0.19), edge computing (0.18), iot security (0.18), android malware (0.17), denial service (0.17), ddos attacks (0.15), fake news detection (0.15) Martins et al. (2020);Manzoor et al. (2019); Lansky et al. (2021); Shinan et al. (2021); Senanayake et al. (2021); Liu et al. (2022); Aiyanyo et al. (2020); Busioc et al. (2020); Rosili et al. (2021); Dakalbab et al. (2022) 2021.7 Ethical Dimensions of AI in Healthcare explainable artificial intelligence (0.20), ai ethics (0.18), intelligence ai (0.18), ai based (0.17), clinical ai (0.17), systematic review (0.17), ai healthcare (0.16), artificial intelligence healthcare (0.16), intelligence healthcare (0.15), artificial intelligence xai (0.15) Milne-Ives et al. (2020);Loh et al. (2022); Xu et al. (2021); Choudhury & Asan (2020); Mathews (2019); Islam et al. (2022); Schachner et al. (2020); Wells & Bednarz (2021); Oh et al. (2021); von Gerich et al. (2022) 2022.0 Intelligent Diagnostics systematic review (0.19), meta analysis (0.18), risk bias (0.15), review meta analysis (0.14), systematic review meta (0.14), machine learning ml (0.13), diabetic retinopathy (0.13), intelligence ai (0.12), machine learning models (0.12), diagnostic accuracy (0.11) Christodoulou et al. (2019);Fleuren et al. (2020); Albahri et al. (2020); Lee et al. (2018); Tayarani N. (2021); Balki et al. (2019); Ahsan & Siddique (2022); Islam et al. (2020); Syeda et al. (2021); Tejedor et al. (2020) 2021.8 Mitigating Bias in Decision Systems gender bias (0.71), gender bias ai (0.51), ai based decision (0.43), bias ai based (0.42), bias artificial intelligence (0.40), gender biases (0.38), mitigating gender bias (0.32), gender bias artificial (0.32), gender bias nlp (0.29), gender biases ml (0.29) Sun et al. (2020); Pagano et al. (2023); Varsha (2023); Shrestha & Das (2022); Nadeem et al. (2022); Reyero Lobo et al. (2023); Hall & Ellis (2023); de Lima et al. (2023); Malheiro et al. (2023); Sengewald & Lackes (2022) 2022.3 ML in Supply Chain and Markets supply chain (0.40), stock market (0.35), market prediction (0.27), stock market prediction (0.25), supply chain management (0.25), literature review (0.23), fraud detection (0.19), predicting stock (0.17), financial time series (0.17), machine learning ml (0.16) Toorajipour et al. (2021);Goodell et al. 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