Current concerns and future directions of large language model ChatGPT in medicine: a machine-learning-driven global-scale bibliometric analysis

作者：Song‐Bin Guo, Dengyao Liu, Xiaojie Fang, Yuan Meng, Zhen-Zhong Zhou, Jing Li, Mei Li, L Q Luo, Hailong Li, Xiu-Yu Cai, Wei-Juan Huang, Xiao-Peng Tian · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000003668 · 被引用次数：63 · 研究领域：Artificial Intelligence in Healthcare and Education、Digital Mental Health Interventions、Machine Learning in Healthcare

BACKGROUND: In its infancy, large language model (LLM) Chat Generative Pre-trained Transformer (ChatGPT) has delivered significant transformational opportunities across an entire healthcare field and is bound to generate an even more impressive impact in the foreseeable future. Nevertheless, it remains fraught with numerous drawbacks and challenges. Therefore, this study aims to determine the current global concerns and future directions of ChatGPT in medicine to inform subsequent research and policymaking. METHODS: This study retrospectively analyzed the global attention and development patterns of ChatGPT in different medical disciplines and geographical regions. Furthermore, based on machine learning algorithms, it revealed the current global concerns and future directions of ChatGPT in the medical field. RESULTS: ChatGPT enjoyed a favorable development trend (Growth Rate Per Month: 26.97%) and global cooperation (International Co-authorships: 25.09%) in medicine. Internal Medicine was the best developed, while Surgery [odds ratio (OR), 0.761; 95% confidence interval (CI), 0.608-0.954; P = 0.018], Health Care (OR, 0.744; 95% CI, 0.583-0.950; P = 0.018), Medical Informatics (OR, 0.622; 95% CI, 0.433-0.893; P = 0.010), Radiology (OR, 0.625; 95% CI, 0.433-0.901; P = 0.012), Public Health (OR, 0.611; 95% CI, 0.416-0.896; P = 0.012), and Oncology (OR, 0.571; 95% CI, 0.346-0.943; P = 0.029) needed further development. The unsupervised hierarchical clustering algorithm divided th...