⚡ Quick Summary
A recent bibliometric analysis revealed that artificial intelligence (AI) applications in general surgery in the United States have seen a significant increase, with a total of 59 studies published between 2020 and 2024. The research primarily focuses on surgical workflow recognition and clinical decision support, highlighting a growing trend in the integration of AI technologies in surgical practices.
🔍 Key Details
- 📊 Total Studies Analyzed: 59 studies (20 reviews, 39 original investigations)
- 📅 Publication Years: 2020-2024
- 🏆 Dominant Research Domains: Surgical workflow recognition (n = 19), Clinical decision support (n = 18)
- 💰 Funding Insights: 57.6% of studies reported no funding
🔑 Key Takeaways
- 📈 Growth Trend: Scientific production increased from 1 study in 2019 to 17 in 2023.
- 💡 Focus Areas: The majority of research is concentrated on workflow and decision-support applications.
- 🔍 Keyword Analysis: AI and machine learning remain prominent, with limited new thematic directions.
- 💰 Funding Distribution: Clinically actionable AI applications are significantly more likely to receive funding (OR 4.0).
- 📊 Statistical Significance: Annual growth in reviews and workflow-focused studies was statistically significant (P < .05).
📚 Background
The integration of artificial intelligence in healthcare, particularly in general surgery, is rapidly evolving. AI technologies are being utilized for various purposes, including preoperative planning, intraoperative guidance, and postoperative management. However, the landscape of AI research in this field has not been thoroughly characterized, prompting the need for a comprehensive analysis of recent trends and funding patterns.
🗒️ Study
This bibliometric analysis was conducted to systematically evaluate the scientific production related to AI in general surgery in the United States over the past five years. The study adhered to established guidelines for reporting bibliometric reviews and included English-language articles published between 2020 and 2025, focusing on AI applications in general surgery with a U.S.-affiliated senior author.
📈 Results
The analysis identified a total of 59 studies that met the inclusion criteria, with a consistent increase in publications over the years. The predominant research domains were surgical workflow recognition and clinical decision support, which together accounted for 63% of the literature. Notably, the study found that most publications reported no funding, indicating a potential gap in financial support for AI research in this area.
🌍 Impact and Implications
The findings of this study underscore the growing importance of AI in enhancing surgical practices. As research continues to expand, there is a clear need for a more equitable distribution of funding to support a broader range of AI applications. This could lead to significant advancements in surgical outcomes and patient care, ultimately transforming the landscape of general surgery.
🔮 Conclusion
The bibliometric analysis highlights the rapid growth of AI research in general surgery, particularly in workflow and decision-support domains. While the focus on clinically actionable applications is promising, the need for broader funding and research directions is evident. As we move forward, fostering innovation in AI technologies will be crucial for improving surgical practices and patient outcomes.
💬 Your comments
What are your thoughts on the integration of AI in general surgery? Do you believe it will significantly change surgical practices in the future? 💬 Share your insights in the comments below or connect with us on social media:
Artificial Intelligence Applications in General Surgery in the United States of America: A Bibliometric Analysis.
Abstract
BACKGROUND: Artificial intelligence (AI) is rapidly transforming surgical practice, with applications spanning preoperative planning, intraoperative guidance, postoperative management, and surgical education. Despite accelerating research activity, the structure, thematic evolution, and funding landscape of AI research in general surgery remain incompletely characterized. This study aimed to systematically evaluate scientific production on AI in general surgery in the United States over the past 5 years using a bibliometric approach.
METHODS: A bibliometric analysis was conducted following Preliminary Guideline for Reporting Bibliometric Reviews of the Biomedical Literature and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines using Web of Science. English-language articles published between 2020 and 2025 with a U.S.-affiliated senior author and focused on AI use in general surgery were included. Publications were analyzed across five primary domains: authorship metrics, thematic endpoints, journal characteristics, country of origin, and funding patterns. Bibliometric indicators included H-index, citation counts, Article Influence Score (AIS), and Bradford’s Law classification. Funding distribution across endpoints was evaluated using chi-square or Fisher’s exact tests, with effect sizes estimated using Cramér’s V and odds ratios. Temporal trends in endpoints and keywords were assessed using Poisson and negative binomial regression models.
RESULTS: Fifty-nine studies met inclusion criteria, comprising 20 reviews and 39 original investigations. Scientific production increased consistently from one study in 2019 to 17 in 2023 and 16 in 2024, demonstrating sustained growth. Surgical workflow recognition (n = 19) and clinical decision support (n = 18) were the predominant research domains, representing 63% of the included literature. Temporal analysis demonstrated significant annual growth in reviews (Incidence rate ratios [IRR] 2.09, P = .002) and workflow-focused studies (IRR 1.37, P = .031). Keyword analysis revealed sustained prominence of AI and machine learning, with limited emergence of new thematic directions. Most studies reported no funding (57.6%). Although overall funding distribution did not significantly differ across application categories (P = .846), clinically actionable AI applications were significantly more likely to receive funding compared with other research areas (OR 4.0, 95% CI 1.22-13.13; P = .029).
CONCLUSION: AI research in U.S. general surgery is growing but remains concentrated in workflow and decision-support domains. Funding favors clinically actionable applications, highlighting the need for broader, equity-focused AI development.
Author: [‘Vasconcellos CAM’, ‘Forchezatto EG’, ‘Lyons G’, ‘Souza E Silva T’, ‘Nogueira R’, ‘Malcher F’, ‘Cavazzola LT’, ‘Lima DL’]
Journal: J Laparoendosc Adv Surg Tech A
Citation: Vasconcellos CAM, et al. Artificial Intelligence Applications in General Surgery in the United States of America: A Bibliometric Analysis. Artificial Intelligence Applications in General Surgery in the United States of America: A Bibliometric Analysis. 2026; (unknown volume):10926429261449958. doi: 10.1177/10926429261449958