ReviewAnnals of medicine and surgery (2012)2025
A bibliometric analysis of large language model-based AI chatbots in surgery.
Review in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Applications of DeepSeek in Medicine: Bibliometric Analysis and Scoping Review.Journal of medical Internet research · 2026Article
- Mapping Artificial Intelligence Research in Oral and Maxillofacial Surgery: A Bibliometric Analysis.International dental journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language model-based artificial intelligence (AI) chatbots are gaining traction in surgery, yet their specific applications remain understudied. This bibliometric analysis assesses research trends and potential applications of large language model-based AI chatbots in surgery. We conducted a search in the Web of Science Core Collection database and analyzed the data using VOSviewer, CiteSpace, and the R package bibliometrix. Out of an initial 1372 papers, 260 met the inclusion criteria. Research output has significantly increased from 2023 to 2024. The United States led in publications, accounting for 52.1% of the total. Harvard Medical School emerged as the leading institution with 13 relevant publications, representing 5% of the overall output. The field comprises 1418 authors, with Seth Ishith, Lechien Jerome, Cho Samuel, and Zaidat Bashar being the most prolific, while Gupta Rohun is the most frequently co-cited author. The analysis of the top 20 keywords reveals that "artificial intelligence" and "ChatGPT" are the most common. Key application areas identified include otolaryngology - head and neck surgery, plastic surgery, neurosurgery, and bariatric surgery, with an emphasis on patient and medical education. AI chatbots in surgery show great potential for advancing patient and medical education. However, the development of sophisticated chatbots capable of facilitating accurate healthcare interactions and delivering personalized care remains a challenge. This analysis provides a comprehensive overview of the current research landscape and highlights areas for future investigation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.