A comprehensive bibliometric analysis of 594 publications from the Web of Science database mapping the current research landscape, emerging hotspots, and future directions of ChatGPT in healthcare research underscores the rapid expansion of scholarly interest in this area.
Abstract
ChatGPT an advanced natural language processing (NLP) model is gaining attention across diverse healthcare contexts. This study presents a comprehensive bibliometric analysis of 594 publications from the Web of Science database mapping the current research landscape, emerging hotspots, and future directions of ChatGPT in healthcare research. The analysis revealed an annual growth rate of 91.18% underscoring the rapid expansion of scholarly interest in this area. Key themes included its potential applications in clinical decision support, medical education and patient communication, as well as recurring concerns related to misinformation and ethical considerations. The most cited publication in this domain received 741 citations reflecting the substantial influence of early contributions. Although ChatGPT can generate human-like text, its potential roles in supporting diagnostic reasoning, streamlining information workflows and assisting in medical documentation remain speculative and require further empirical validation. This study provides a descriptive foundation to guide future interdisciplinary inquiries into ChatGPT’s evolving role in healthcare.
This paper conducts a comprehensive analysis of evaluation methods, deployment processes, and governance strategies for LLMs in the healthcare field, focusing on three key issues: model version drift, multilingual external validation, and prompt injection security governance.
Song-Bin Guo, Sui-Xing Zhong, Yixian Ma et al.· International Journal of Sur...· 0 citations
Current evidence indicates that LLMs have substantial potential to enhance healthcare delivery, research, and personalized medicine, but they should currently be regarded as supportive tools rather than autonomous clinical decision-makers.
Antoni Klamka, Paulina Kawalec, Kamil Bronikowski et al.· 0 citations
Language barriers hinder healthcare, particularly during case history-taking, a key part of diagnosis. While multilingual artificial intelligence (AI) chatbots offer solutions, there is fragmented evidence of their effectiveness and impact. This systematic review followed PRISMA 2020 guidelines, examining studies published between 2015 and 2025 on multilingual AI chatbots in healthcare across four databases (Google Scholar, Scopus, Web of Science, and PubMed), using a two-stage screening process. Data extraction focused on applications, supported languages, underlying technologies, target populations, and clinical outcomes. From 503 records, 49 studies, covering primary care, telemedicine, oncology, mental health, and other areas, met the criteria. Supported languages included English, Spanish, Arabic, Chinese, Hindi, and other underrepresented languages. In individual system evaluations using heterogeneous methodologies and evaluation settings, AI chatbots achieved a diagnostic accuracy ranging from 72–92%. Core technologies included large language models (LLMs), bidirectional encoder representations from transformers (BERT), a generative pre-trained transformer (GPT), retrieval-augmented generation (RAG), speech recognition, and distillation. The findings show that these improve clinical workflow (30–70% time savings) and patient engagement, reduce language barriers, and promote health equity. However, the overall evidence certainty was low to moderate, reflecting the predominance of prototype and proof-of-concept studies. Multilingual AI chatbots demonstrate a boost in healthcare efficiency, a reduction in language barriers, and the promotion of health equity, but exhibit challenges regarding validation, workflow integration, and evaluation standards, along with ethical issues such as privacy and bias. Future research should include real-world studies, diverse populations, standardized outcome measures, and long-term equity assessments.
R. Sharanesha, Deepti Virupakshappa, A. Abushanan et al.· Informatics· 0 citations
A scoping review of 24 PubMed-indexed studies published between 2023 and 2026 was conducted to assess current applications, benefits, limitations, and future directions of LLMs in healthcare.
Antoni Klamka, Paulina Kawalec, Kamil Bronikowski et al.· Quality in Sport· 0 citations
Artificial intelligence (AI) has increasingly become a central component of healthcare research, yet a systematic understanding of its scholarly evolution remains limited. This study presents a comprehensive bibliometric analysis of 594 peer-reviewed publications indexed in Scopus between 2012 and April 2024, retrieved using a focused title-based search strategy. Using VOSviewer, we examine publication and citation trends, leading contributing countries and institutions, and thematic structures through keyword co-occurrence networks. Results reveal a marked acceleration in research output after 2018, with the United States, China, and the United Kingdom emerging as dominant contributors and central hubs in international collaboration networks. Keyword analysis indicates a strong methodological emphasis on machine learning, deep learning, and medical imaging, while comparatively limited attention is given to ethical, implementation, and equity-related themes. These findings highlight both the rapid growth and the thematic concentration of AI-in-healthcare research, underscoring the need for future studies to address translational and governance challenges alongside technical innovation.
Abdulaziz Yasin Nageye, Abdukadir Dahir Jimale, Mohamed Omar Abdullahi et al.· Discover Internet of Things· 0 citations