Objective: With the rapid advancement of Chat Generative Pre-Trained Transformer (ChatGPT) in medical research, our study aimed to identify global trends and focal points in this domain. Method: All publications on ChatGPT in medical research were retrieved from the Web of Science Core Collection (WoSCC) by Clarivate Analytics from January 1, 2023, to January 31, 2024. The research trends and focal points were visualized and analyzed using VOSviewer and CiteSpace. Results: A total of 1,239 publications were collected and analyzed. The USA contributed the largest number of publications (458, 37.145%) with the highest total citation frequencies (2,461) and the largest H-index. Harvard University contributed the highest number of publications (33) among all full-time institutions. The Cureus Journal of Medical Science published the most ChatGPT-related research (127, 10.30%). Additionally, Wiwanitkit V contributed the majority of publications in this field (20). "Artificial Intelligence (AI) and Machine Learning (ML)," "Education and Training," "Healthcare Applications," and "Data Analysis and Technology" emerged as the primary clusters of keywords. These areas are predicted to remain hotspots in future research in this field. Conclusion: Overall, this study signifies the interdisciplinary nature of ChatGPT research in medicine, encompassing AI and ML technologies, education and training initiatives, diverse healthcare applications, and data analysis and technology advancements. These areas are expected to remain at the forefront of future research, driving continued innovation and progress in the field of ChatGPT in medical research.
BACKGROUND: Hepatic cancer is a common cancer in clinical practice. Current drug therapies for this condition include targeted therapy, chemotherapy, and immunotherapy. Tumor lysis syndrome (TLS) is the most serious complication of oncology treatment. According to the literature, several cases reported TLS occurred with targeted therapies for hepatic cancer. METHODS: Reporting odds ratio and information component were used to measure the disproportionate signals for TLS associated with targeted therapies, using data from the FDA's Adverse Event Reporting System (FAERS). A stepwise sensitivity analysis was conducted to test the robustness of signals. Time-to-onset analysis was used to describe the latency of TLS events associated with targeted therapies. The Bradford Hill criteria were used to perform a global assessment of the evidence. RESULTS: Sorafenib, lenvatinib, cabozantinib, and bevacizumab showed higher disproportionate signals for TLS than chemotherapy. The median number of days to TLS occurrence after drug therapy was 5.5, 6.5, and 6.5 days for sorafenib, lenvatinib, and bevacizumab, respectively. CONCLUSIONS: There is a significant association between tumor lysis syndrome and targeted therapies for hepatic carcinoma, with particularly strong signals for sorafenib and lenvatinib. Clinicians should be aware of the potential for tumor lysis syndrome in targeted therapies for hepatic carcinoma.
