This introductory text highlights key insights from a recent bibliometric study titled “Global research trends in the application of artificial intelligence in oncology care: a bibliometric study”. Published on January 7, 2025, by Xu M, Chen Y, Wu T, Chen Y, Zhuang W, Huang Y, and Chen C, this open-access article provides a comprehensive analysis of the evolving landscape of artificial intelligence (AI) in oncology nursing and care.
The study, which analyzed 517 English-language articles from 1994 to 2024 retrieved from the Web of Science database, aimed to map the prospects and development trends of AI in oncology nursing. It reveals that the field is currently entering a stage of rapid development, particularly evident in the significant increase in publications from 2020 to 2024, indicating growing academic attention and advancements in AI technologies. This rapid growth phase suggests that research on AI in oncology care will likely remain active and robust in the coming years.
Key findings from the bibliometric analysis include:
- Leading Countries and Institutions: The United States leads in publication output, accounting for 36.4% of articles (188 publications), followed by China with 15.3% (79 publications). All of the top 10 institutions in terms of publication output are U.S.-based, with Harvard University ranking first with 29 publications. Despite China’s second rank in publications, it has fewer top institutions in the top 20, indicating a need for enhanced research competitiveness through domestic and international cooperation.
- Prominent Research Areas and Hot Topics: The most frequently studied tumor type is breast cancer, appearing in 71 papers. Core research topics frequently involve “artificial intelligence” (131 times), “machine learning” (103 times), and “deep learning”. Recent hotspots, identified through keyword burst analysis, include “model” (2021-2024) and “human papillomavirus” (2022-2024). Other keywords that have gained considerable attention recently include “deep learning” (2019-2022) and “trend” (2021-2022).
- Applications and Future Prospects: AI is anticipated to significantly impact early detection, diagnosis, treatment, survival, and palliative care for patients, caregivers, and clinicians. Future research is expected to focus on several key areas, including:
- Health Education and Awareness: Utilizing chatbots, game-based learning tools, animated videos, and social media to promote health-related knowledge.
- Symptom Management: Employing AI and the Internet of Things (IoT) for remote and real-time symptom detection and management, enhancing symptom control outside the hospital.
- Care Coordination: Assisting nurse navigators in guiding cancer patients through care pathways, predicting emergencies, and automating the planning of care pathways and resource allocation.
- Drug Preparation and Injection: Advancing AI-powered drug preparation systems and intelligent analysis for home-based treatment of biologics, enhancing safety and guiding treatment decisions.
- Decision Support: Improving prognosis assessments by linking histological features to clinical outcomes, facilitating precision oncology approaches, and integrating multimodal data for comprehensive analysis.
- Clinical Practice Validation: Incorporating AI tools into clinical practice only after predictive models have been clinically validated, and addressing privacy concerns and clinical accountability.
Despite the promising advancements, the study also highlights significant challenges for the broader application of AI in cancer care:
- Ethical Concerns: Issues related to data privacy (e.g., risk of data leakage with EHRs), algorithmic bias (e.g., misdiagnosis for underrepresented groups), and the potential impact on the patient-provider relationship.
- Explainability: The “black box” nature of AI limits clinicians’ ability to interpret its decisions, underscoring the need for interpretable models to build trust.
- Generalizability and Integration: Challenges in ensuring generalizability across diverse patient populations (e.g., different races, genders, socioeconomic groups) and seamlessly integrating AI systems into existing clinical workflows while maintaining ease of use and efficiency.
- Training and Data Standardization: The necessity for additional training for cancer nurses in machine learning and natural language processing, and the current lack of standardized health data in oncology, which hampers the validation and applicability of AI algorithms.
This comprehensive bibliometric analysis serves as a valuable reference, systematically revealing global research trends and providing insights for future research and practice in the rapidly evolving field of AI in oncology care, ultimately aiming for more intelligent and personalized medical services.
Reference for the article:
Xu, M., Chen, Y., Wu, T., Chen, Y., Zhuang, W., Huang, Y., & Chen, C. (2025). Global research trends in the application of artificial intelligence in oncology care: a bibliometric study. Frontiers in Oncology, 14, 1456144. https://doi.org/10.3389/fonc.2024.1456144
