A scoping review of artificial intelligence in acute care surgerypromise, pitfalls, and a path forward
While AI holds promise for enhancing ACS, current applications are narrowly focused on pre-operative risk prediction using limited data modalities. Future efforts must prioritize prospective validation, real-time dynamic predictions, clinician-centered design, and multimodal data integration to realize AI's potential for improving ACS patient outcomes in time-sensitive, high-acuity surgical settings.
Authors
- Carla M. Pugh
- Rachel L. Choron (ORCID: https://orcid.org/0000-0002-2297-9956)
- Amanda L. Teichman (ORCID: https://orcid.org/0000-0003-4120-8345)
- Gabriel A. Brat (ORCID: https://orcid.org/0000-0003-3928-5931)
- Hana Ben Ali
- Jeffrey K. Jopling (ORCID: https://orcid.org/0000-0002-0694-1704)
- Divya Kewalramani (ORCID: https://orcid.org/0009-0004-9125-0909)
- Sruti Cheruvu
- Kaustav Chattopadhyay
- Mayur Narayan
- Tyler J Loftus
- Justin Benton (ORCID: https://orcid.org/0009-0006-3006-0532)
- Amin Madani
- Julia Coleman
- Swati Mylarappa
- Advika Joshi
- Gowthami Vidhya
- Shaina Anuncio
- Philip S. Barie
- Julian Varas Cohen
- Jason Hua
Institutions
- Rutgers, The State University of New Jersey (US)
- Beth Israel Deaconess Medical Center (US)
- University Health Network (CA)
- Johns Hopkins University (US)
- Pontificia Universidad Católica de Chile (CL)
- University of Toronto (CA)
- Cornell University (US)
- Johns Hopkins Medicine (US)
- University of Florida Health (US)
- Johnson University (US)
- Stanford Medicine (US)
- Artificial Intelligence in Medicine (Canada) (CA)
- Weill Cornell Medicine (US)
- The Ohio State University (US)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-11-25
- DOI
- https://doi.org/10.1097/js9.0000000000003794
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00