The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Artificial intelligence in surgery: a global balancing act
Open accessAbstract
In the contemporary landscape of surgery, artificial intelligence (AI) plays markedly different roles across the global economic spectrum, offering solutions that range from reactive to predictive. In affluent nations, AI applications are predominantly predictive, proactively addressing potential health issues before they manifest. Conversely, in low- and middle-income countries (LMICs), as highlighted by Muir Gray, urgent challenges like unwarranted variability in surgical quality, harm, resource waste, inequity and lack of prevention prevail1. These issues necessitate a strategic shift in AI application towards reactive solutions that directly address these immediate and critical surgical concerns. This dichotomy underscores the need for a strategic realignment of AI applications in surgery, moving beyond collaborative efforts to include major transformations in business models, surgical culture and technology. This article describes a comprehensive approach to aligning technology with the diverse needs of the global population, thus fostering a more balanced and responsive global surgical ecosystem. Emerging AI techniques show promise across the surgical value chain, from self-service educational tools for community health to computer-assisted intraoperative and postoperative care. Additionally, AI’s role in administrative tasks like resource planning and inventory management could streamline healthcare services. The early deployment of AI in surgery should focus on enhancing patient outcomes and increasing access in underserved areas. These multifaceted applications, however, hinge on governance that ensures equitable adoption and practical integration2. Prioritizing these actions lays the groundwork for systemic healthcare reform underpinned by transparent and altruistic Global North–South partnerships.
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Advancing Patient‐Centred AI With Adaptive Machine Learning
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Metadata
- Authors
- Kewalramani, D., Loftus, T. J., Mayol, J., & Narayan, M.
- Affiliations
- Children's Hospital of Pittsburgh (US)
- University of Florida (US)
- Universidad Complutense de Madrid (ES)
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Kewalramani, Divya
- Venue
- British Journal of Surgery
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-03
- Volume
- 111
- Issue
- 3
- ISSN
- 0007-1323 (ISSN-L)
- 1365-2168
- OpenAlex
- W4393166134
- Cited by
- 20
- References
- 6
- Citations by year
- 2026: 12 · 2025: 7 · 2024: 1
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British journal of surgery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Global Health and Surgery
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Keywords
- artificial intelligence in surgery, low- and middle-income countries, global health equity, surgical training, computer-assisted surgery, postoperative care, healthcare resource planning, AI governance, access to surgical care
- MeSH terms
- Artificial Intelligence, Humans, General Surgery
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