⚡ Quick Summary
This bibliometric analysis explores the application of artificial intelligence (AI) in palliative care, revealing a significant surge in research output from 2020 to 2024. The study identifies key trends, including a focus on machine learning and deep learning, which hold promise for enhancing patient quality of life and optimizing care plans.
🔍 Key Details
- 📊 Dataset: 246 publications from 45 countries
- 🧩 Key technologies: AI, machine learning, deep learning, natural language processing
- ⚙️ Analysis tools: HistCite, VOSviewer, CiteSpace
- 🏆 Leading contributors: U.S. and Journal of Pain and Symptom Management
🔑 Key Takeaways
- 📈 Research output in AI-driven palliative care has surged notably since 2020.
- 🌍 International collaboration is crucial, particularly between the U.S. and China.
- 🔑 Key research areas include deep learning, neural networks, and quality-of-life enhancement.
- 🤖 Emerging trends emphasize the integration of machine learning in clinical practice.
- 🏥 Prominent institutions like Harvard Medical School are leading the way in this research.
- 💡 The field remains in its early stages, indicating vast potential for future studies.
- 📚 The study highlights the need for interdisciplinary collaboration to foster innovation.
📚 Background
Palliative care plays a vital role in enhancing the quality of life for patients with serious illnesses. However, it faces numerous challenges, including resource limitations and workforce shortages. The integration of artificial intelligence offers promising solutions to optimize symptom management and personalize care plans, yet existing research often focuses on isolated technologies rather than a comprehensive evaluation of their developmental trajectory.
🗒️ Study
This study utilized bibliometric methods to analyze research trends in AI-driven palliative care, mapping knowledge structures and identifying research hotspots. Data was sourced from the Web of Science Core Collection, covering publications up to February 28, 2024. The analysis employed tools like HistCite for bibliometric aggregation and VOSviewer for co-occurrence analysis.
📈 Results
The analysis revealed a total of 246 publications from 615 institutions and 1,456 authors. Notably, research output surged between 2020 and 2024, with the U.S. leading contributions. Keyword analysis highlighted a focus on deep learning, neural networks, and AI model development, indicating a shift towards machine learning and holistic AI integration in palliative care.
🌍 Impact and Implications
The findings of this study underscore the transformative potential of AI in palliative care. By enhancing patient quality of life and enabling personalized treatment plans, AI technologies can significantly improve care delivery. The emphasis on interdisciplinary collaboration and the integration of technology with clinical practice is essential for fostering innovation in this field.
🔮 Conclusion
This bibliometric analysis highlights the growing importance of artificial intelligence in palliative care, revealing both current trends and future opportunities for research. As the field continues to evolve, there is a clear need for further studies that focus on improving patient outcomes through innovative AI applications. The future of palliative care looks promising with the integration of advanced technologies!
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Application of artificial intelligence in palliative care: a bibliometric analysis of research hotspots and trends.
Abstract
BACKGROUND: Palliative care, essential for improving quality of life in patients with serious illnesses, faces challenges such as resource limitations, workforce shortages, and the complexity of personalized care. AI’s capabilities in data analysis and decision-making offer opportunities to optimize symptom management, predict end-of-life risks, and tailor care plans. However, existing research emphasizes isolated AI technologies rather than systematic evaluations of its developmental trajectory in palliative care, particularly through bibliometric and visualization studies. This gap obscures trends in technological applications, interdisciplinary collaboration pathways, and research hotspots, potentially hindering AI’s practical innovation in the field.
OBJECTIVE: This study employs bibliometric methods to analyze research trends in AI-driven palliative care, mapping knowledge structures and identifying hotspots to inform future advancements.
METHODS: Data from the Web of Science Core Collection (inception to February 28, 2024) were analyzed using HistCite for bibliometric aggregation, VOSviewer for co-occurrence analysis, and CiteSpace for keyword trends.
RESULTS: Among 246 publications from 45 countries, 615 institutions, and 1,456 authors, output surged notably between 2020 and 2024. The U.S. and the Journal of Pain and Symptom Management led contributions. Keyword analysis highlighted research foci on deep learning, neural networks, quality-of-life enhancement, survival prediction, AI model development, and clinical optimization. Emerging trends emphasize machine learning and holistic AI integration.
CONCLUSION: Despite the increasing number of related studies in recent years, the field remains in its early developmental stage, indicating vast potential for further research. Studies have shown that international collaboration, particularly between the United States and China, is crucial for enhancing global academic influence. Prominent institutions in the United States, such as Harvard Medical School and the University of Pennsylvania, have led research in this area, while the involvement of other countries, especially developing nations, still requires strengthening. Technological analyses reveal that machine learning, deep learning, and natural language processing are becoming increasingly significant in palliative care. Future research will focus on improving patient quality of life, personalized treatment, and disease prognosis prediction, with an emphasis on interdisciplinary collaboration and the integration of technology with clinical practice to foster the innovative development of artificial intelligence in palliative care.
SYSTEMATIC REVIEW REGISTRATION: https://osf.io/, identifier https://doi.org/10.17605/OSF.IO/YCHNQ.
Author: [‘Pan M’, ‘Huang R’, ‘Liu C’, ‘Xiong Y’, ‘Li N’, ‘Peng H’, ‘Liang Y’, ‘Gu W’, ‘Liu H’]
Journal: Front Med (Lausanne)
Citation: Pan M, et al. Application of artificial intelligence in palliative care: a bibliometric analysis of research hotspots and trends. Application of artificial intelligence in palliative care: a bibliometric analysis of research hotspots and trends. 2025; 12:1597195. doi: 10.3389/fmed.2025.1597195