Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review

BACKGROUND

Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored.

PURPOSE

This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs.

METHODS

A scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024.

RESULTS

The findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency.

CONCLUSION

Cancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention

Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.

Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods

Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide. With recent technological advances, artificial intelligence (AI) has begun to play a crucial role in improving diagnostic and treatment methods. It is crucial to understand how AI has integrated into LC research and to identify the main areas of focus. The aim of the study was to provide an updated insight into the role of AI in LC research, analyzing evolving topics, geographical distribution, and contributions to journals. The study explores research trends in AI applied to LC through a novel approach combining latent Dirichlet allocation (LDA) topic modeling with the HJ-Biplot statistical technique. A growing interest in AI applications in LC oncology was observed, reflected in a significant increase in publications, especially after 2017, coinciding with the availability of computing resources. Frontiers in Oncology leads in publishing AI-related LC research, reflecting rigorous investigation in the field. Geographically, China and the United States lead in contributions, attributed to significant investment in R&D and corporate sector involvement. LDA analysis highlights key research areas such as pulmonary nodule detection, patient prognosis prediction, and clinical decision support systems, demonstrating the impact of AI in improving LC outcomes. DL and AI emerge as prominent trends, focusing on radiomics and feature selection, promising better decision-making in LC care. The increase in AI-driven research covers various topics, including data analysis methodologies, tumor characterization, and predictive methods, indicating a concerted effort to advance LC research. HJ-Biplot visualization reveals thematic clustering, illustrating temporal and geographical associations and highlighting the influence of high-impact journals and countries with advanced research capabilities. This multivariate approach offers insights into global collaboration dynamics and specialization, emphasizing the evolving role of AI in LC research and diagnosis.

An omics-based tumor microenvironment approach and its prospects

Multi-omics approaches are revolutionizing cancer research and treatment by integrating single-modality omics methods, such as the transcriptome, genome, epigenome, epi-transcriptome, proteome, metabolome, and developing omics (single-cell omics). These technologies enable a deeper understanding of cancer and provide personalized treatment strategies. However, challenges such as standardization and appropriate methods for funneling complex information into clinical consequences remain. The tumor microenvironment (TME) is a complex system containing cancer cells, immune cells, stromal cells, and secreted molecules. To overcome these challenges, researchers can establish standardized protocols for data collection, analysis, and interpretation. Collaborations and data sharing among research groups and institutions can create a comprehensive and standardized multi-omics database, facilitating cross-validation and comparison of results. Multi-omics profiling enables in-depth characterization of diversified tumor types and better reveal their function in cancer immune escape. Datasets play a fundamental role in multi-omics approaches, with artificial intelligence and machine learning (AI) rapidly advancing in multi-omics for cancer.

Global research and current trends on nanotherapy in lung cancer research: a bibliometric analysis of 20 years

BACKGROUND

Lung cancer ranks as one of the most rapidly growing malignancies. Which is characterized by its poor prognosis and a low survival rate due to late diagnosis and limited efficacy of conventional treatments. In recent years nanotechnology has emerged as a promising frontier in the management of lung cancer, presenting novel strategies to enhance drug administration, improve therapeutic efficiency, and mitigate side effects. This research comprehensively evaluates the current state and research trends concerning the application of nanomaterials in lung cancer through bibliometric analysis.

MATERIALS AND METHODS

We employed a systematic approach by retrieving studies from the Scopus database that focused on nanomaterials and lung cancer between 2003 and 2023. Subsequently, we carefully selected relevant articles based on predetermined inclusion criteria. The selected publications were then subjected to bibliometric and visual analysis using softwares such as VOSviewer and Biblioshiny.

RESULTS

A total of 3523 studies that meet inclusion criteria were selected for bibliometric analysis. We observed a progressive increase in the number of annual publications from 2003 to 2023, indicating the growing interest in this field. According to our analysis, China is the primary contributor to publication output among the countries. The "Ministry of Education of the People's Republic of China" was the most influential institution. Among the authors, "Dr. Jack A. Roth" and "Dr. Huang Leaf" had the highest number of publications and cited publications, respectively. The "International Journal of Nanomedicine" was found to be the most prolific journal in this field. Additionally, "Biomaterials" emerged as the most cited journal. Through keyword analysis, we identified five main research themes and future research directions; nono-immunotherapy and green synthesis are the hot topics in this research field.

CONCLUSION

Our study summarized the key characteristics of publications in this field and identified the most influential countries, institutions, authors, journals, hot topics, and trends related to the application of nanomaterials in lung cancer. These findings contribute to the existing body of knowledge and serve as a foundation for future research endeavors in this area. More effective efforts are needed in this field to reduce the burden of lung cancer and help achieve the United Nation's Sustainable Development Goals.

Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

Indirect effects of health-related quality of life on suicidal ideation through psychological distress among cancer patients

The interrelationships of suicidal ideation, psychological distress, and impaired health-related quality of life (HRQoL) in cancer patients are complex and multifaceted. Limited empirical evidence exists on the indirect effects of impaired HRQoL on suicidal ideation through psychological distress among cancer patients. To fill this research gap, 250 cancer patients were recruited through a cross-sectional hospital-based research design. Structural equation model (SEM) results indicated that impaired HRQoL is a predictor of psychological distress (β = 0.153; p < 0.05), and psychological distress positively predicts suicidal ideation (β = 0.647; p < 0.000). The study found no direct effects of impaired HRQoL on suicidal ideation (β = -0.05; p = 0.223). Indirect effects of HRQoL on suicidal ideation was confirmed, showing a full-mediation effect β = 0.099 (SE = 0.048, CI = [0.030, 0.189], p < 0.05) (i.e. the pathway impaired HRQoL predict suicidal ideation is through psychological distress). Cognitive-behavioral therapy and other emotional support programs should be considered for cancer patients to mitigate psychological vulnerabilities linking impaired HRQoL to suicidal ideation.

How can physicians adopt AI-based applications in the United Arab Emirates to improve patient outcomes?

Objective

The enabling and derailing factors for using artificial intelligence (AI)-based applications to improve patient care in the United Arab Emirates (UAE) from the physicians' perspective are investigated. Factors to accelerate the adoption of AI-based applications in the UAE are identified to aid implementation.

Methods

A qualitative, inductive research methodology was employed, utilizing semi-structured interviews with 12 physicians practicing in the UAE. The collected data were analyzed using NVIVO software and grounded theory was used for thematic analysis.

Results

This study identified factors specific to the deployment of AI to transform patient care in the UAE. First, physicians must control the applications and be fully trained and engaged in the testing phase. Second, healthcare systems need to be connected, and the AI outcomes need to be easily interpretable by physicians. Third, the reimbursement for AI-based applications should be settled by insurance or the government. Fourth, patients should be aware of and accept the technology before physicians use it to avoid negative consequences for the doctor-patient relationship.

Conclusions

This research was conducted with practicing physicians in the UAE to determine their understanding of enabling and derailing factors for improving patient care through AI-based applications. The importance of involving physicians as the accountable agents for AI tools is highlighted. Public awareness regarding AI in healthcare should be improved to drive public acceptance.

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Classification and citation analysis of the 100 top-cited articles on nurse resilience using chord diagrams: A bibliometric analysis

BACKGROUND

Studies of most-cited articles have been frequently conducted on various topics and in various medical fields. To date, no study has examined the characteristics of articles associated with theme classifications and research achievements of article entities related to nursing resilience. This study aims to graphically depict the characteristics of the 100 top-cited articles addressing nurse resilience (T100NurseR), diagram the relationship between articles and author collaborations according to themes extracted from article keywords, and examine whether article keywords are correlated with article citations.

METHODS

T100NurseR publications were retrieved from the Web of Science (WoS) core collection on October 13, 2022. Themes associated with articles were explored using coword analysis in WoS keywords plus. The document category, journal ranking based on impact factor, authorship, and L-index and Y-index were used to analyze the dominant entities. To report the themes of T100NurseR and their research achievements in comparison to article entities and verify the hypothesis that keyword mean citation can be used to predict article citations, 5 visualizations were applied, including network diagrams, chord diagrams, dot plots, Kano diagrams, and radar plots.

RESULTS

Citations per article averaged 61.96 (range, 25-514). There were 5 themes identified in T100NurseR, including Parses theory, nurse resilience, conflict management, nursing identity, and emotional intelligence. For countries, institutes, departments, and authors in comparison of category, journal impact factor, authorship, and L-index scores, Australia (129.80), the University of Western Sydney (23.12), Nursing (87.17), and Kim Foster (23.76) are the dominant entities. The weighted number of citations according to Keywords Plus in WoS is significantly correlated with article citations (Pearson R = 0.94; P = .001).

CONCLUSION

We present diagrams to guide evidence-based clinical decision-making in nurse resilience based on the characteristics of the T100NurseR articles. Article citations can be predicted using weighted keywords. Future bibliographical studies may apply the 5 visualizations to relevant studies, not being solely restricted to T100NurseR.

Imaging in translational cancer research

This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

A systematic and thematic analysis of the top 100 cited articles on mRNA vaccine indexed in Scopus database

The success of mRNA vaccines against SARS-CoV-2 implies that this technology can be applied to target any pathogen. However, the scientific production and research trends using the bibliometric method are still unknown. The top 100 most cited articles on mRNA vaccine research were obtained from the Scopus database from 1995 to 2021. Bibliometrix, an R-Package, and VOSviewer 1.6.11 were used for data analysis. There is a rapid growth in scientific outputs with a gradual increase in 2021. The United States produced 45 (45%) of the articles, followed by Germany with 15 (15%) and Israel with 10 (10%). The New England Journal of Medicine published the most papers in this field 13 (13%), followed by Nature 6(6%). Barney S. Graham was the most productive author among the top 100 most cited mRNA vaccine articles. University of Pennsylvania Perelman School of Medicine, US, was the top ranking institution, having 37 (37%). The visualization map clearly and spontaneously displayed the current state and research hot spots of mRNA research from a specific perspective. The most frequent keywords were COVID-19, vaccine, mRNA vaccine, mRNA, SARS-CoV-2, and immunogenicity, among others. A systematic review of the articles provided evidence that out of 100 articles, approximately 25 (25%) were focused on vaccine production and evaluation, followed by 26 (26%) in mRNA vaccine safety and efficacy, 23 (23%) were into mRNA vaccination, 23 (23%) considered risk factors associated with mRNA vaccination, while 8 (8%) of the articles covered the issue of mRNA vaccine delivery. In addition, 42% of the articles focused on COVID-19, 17% on cancer, 8% on influenza virus, 4% on COVID-19 and kidney disease, 3% COVID-19 and myocarditis, and 3% on rabies virus, among others. The findings of this systematic and thematic analysis provided the knowledge basis for further research on mRNA vaccines globally.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Objective

Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.

Methods

On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.

Results

A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General &amp; Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.

Conclusions

An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.