Bibliometric analysis of publications discussing the use of the artificial intelligence technique agent-based models in sustainable agriculture

Sánchez, Juan Manuel; Rodríguez, Juan ́Pablo; Espitia, Helbert E

doi:10.1016/j.heliyon.2022.e12005

Cited by 12 publications

(7 citation statements)

References 103 publications

(130 reference statements)

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“…Looking ahead, AI in lung imaging is poised for further expansion with advancements in multimodal models integrating patient data streams, foundational AI models trained on extensive datasets, and specialized medical AI models tailored for specific conditions such as lung cancer. However, addressing challenges such as regulation, quality assurance, data diversity, and transparency in AI algorithms will be critical for maximizing the potential benefits of AI in lung imaging while safeguarding patient safety and ensuring equitable access to advanced healthcare technologies [ 60 - 62 ]. Figure 3 shows challenges and limitations.…”

Section: Reviewmentioning

confidence: 99%

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Sindhu,

Jadhav,

Ghewade

et al. 2024

Cureus

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Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

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Section: Reviewmentioning

confidence: 99%

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Sindhu,

Jadhav,

Ghewade

et al. 2024

Cureus

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“…In the context of identifying research gaps, ML techniques may facilitate the discovery of research gaps by analyzing large volumes of scientific evidence in a systematic, scalable, and efficient manner. To understand the current status quo of scientific evidence available, several studies leveraged ML to perform natural language processing, bibliometric analysis, and text mining, which have yielded promising results in several domains, including health care [9][10][11][12], social sciences [16][17][18][19], and environmental sciences [20]. However, the application of ML and its potential for identifying research gaps in rapidly evolving fields remains underexplored.…”

Section: Research Problem and Aimmentioning

confidence: 99%

Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study

Abd-alrazaq,

Nashwan,

Shah

et al. 2024

JMIR Form Res

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Background Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. Objective In this paper, we propose a machine learning–based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. Methods We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). Results After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: “virus of COVID-19,” “risk factors of COVID-19,” “prevention of COVID-19,” “treatment of COVID-19,” “health care delivery during COVID-19,” “and impact of COVID-19.” The most prominent topic, observed in over half of the analyzed studies, was “the impact of COVID-19.” Conclusions The proposed machine learning–based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

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“…In ref. [6], a literature review was conducted on artificial intelligence and agriculture. To explore research on technology adoption in agriculture and understand the determinants of technology adoption using models, such as the technology acceptance model, a bibliometric analysis was conducted [7].…”

Section: Bibliometric Survey On Digital Agriculturementioning

confidence: 99%

A Survey on Digital Agriculture in Five West African Countries

et al. 2023

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This study focuses on agriculture, which is the main source of economic growth in many West African countries. In recent years, conventional agriculture has undergone a remarkable evolution and digital technologies are widely used for different purposes. While the world is rapidly using advanced digital technologies to grow their agriculture, Africa seems to be lagging behind, especially West Africa. To know how to contribute effectively, it is important to know what is being performed about this issue. The objective of this study is to examine the state of digital agriculture in five countries, namely, Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Nigeria. The study consisted of an analysis of the scientific contributions of these countries and the cases of actual deployment. This is carried out by means of a bibliometric study based on data collected from the Web of Science and a comparative review of the technologies used in the target countries using data from several sources, such as IEEE, Scopus, Science Direct, Google Scholar, etc. The bibliometric analysis based on 3249 publications revealed that research interests have increased significantly since 2014. Climate change, machine learning (ML), and adoption have been the hottest topics of discussion and most of the organizations working on the topic are academic bodies. Moreover, a considerable amount of the scientific input was obtained from Nigeria, which is the most populous of the five countries considered. The survey on digital farming showed that publications in Nigeria that address deployment cases were focused on the internet of things (IoT), wireless sensor networks, blockchain, and artificial intelligence (AI) technologies. In Ghana, practical cases of blockchain, AI, and big data deployment were observed, while Burkina Faso focused on IoT and AI. In Côte d’Ivoire and Benin, the deployment cases generally focused on AI.

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Bibliometric analysis of publications discussing the use of the artificial intelligence technique agent-based models in sustainable agriculture

Cited by 12 publications

References 103 publications

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study

A Survey on Digital Agriculture in Five West African Countries

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