TY - JOUR
T1 - Navigating the artificial intelligence landscape in trauma, critical care and emergency general surgery
T2 - insights from the American Association for the Surgery of Trauma (AAST) 2025 Annual Meeting Panel Discussion
AU - Kewalramani, Divya
AU - Jawa, Randeep
AU - Cuschieri, Joseph
AU - Kaafarani, Haytham
AU - Buchman, Timothy G.
AU - Narayan, Mayur
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: https://creativecommons.org/licenses/by-nc/4.0/.
PY - 2026/1
Y1 - 2026/1
N2 - Artificial intelligence (AI) is rapidly transforming acute care surgery (ACS), encompassing trauma, emergency general surgery, and critical care. This article synthesizes key insights from the “Artificial Intelligence in Surgery: The Future is Now” panel session at the 2025 American Association for the Surgery of Trauma Annual Meeting. Panelists discussed current clinical applications including large language models for documentation and evidence synthesis, physiologic foundation models for intensive care unit monitoring, AI-enhanced feedback systems for surgical education, video-based performance analytics, and interpretable risk prediction tools. Emerging technologies including digital twins, augmented reality navigation, and AI-enabled robotics were also examined. Cross-cutting themes emphasized interpretability over opaque “black-box” models, rigorous bias auditing, and the critical importance of external validation and pragmatic human versus human plus AI study designs. Implementation requires robust data infrastructure, institutional governance, and staged deployment prioritizing augmentation over automation. The panel concluded that responsible AI adoption in ACS rests on three pillars: rigorous evaluation standards commensurate with clinical influence, institutional investment in infrastructure and “algorithmic stewardship,” and AI literacy as a core professional competency. Meeting these conditions positions AI to reduce administrative burden and support more precise, equitable care for acutely ill and injured patients.
AB - Artificial intelligence (AI) is rapidly transforming acute care surgery (ACS), encompassing trauma, emergency general surgery, and critical care. This article synthesizes key insights from the “Artificial Intelligence in Surgery: The Future is Now” panel session at the 2025 American Association for the Surgery of Trauma Annual Meeting. Panelists discussed current clinical applications including large language models for documentation and evidence synthesis, physiologic foundation models for intensive care unit monitoring, AI-enhanced feedback systems for surgical education, video-based performance analytics, and interpretable risk prediction tools. Emerging technologies including digital twins, augmented reality navigation, and AI-enabled robotics were also examined. Cross-cutting themes emphasized interpretability over opaque “black-box” models, rigorous bias auditing, and the critical importance of external validation and pragmatic human versus human plus AI study designs. Implementation requires robust data infrastructure, institutional governance, and staged deployment prioritizing augmentation over automation. The panel concluded that responsible AI adoption in ACS rests on three pillars: rigorous evaluation standards commensurate with clinical influence, institutional investment in infrastructure and “algorithmic stewardship,” and AI literacy as a core professional competency. Meeting these conditions positions AI to reduce administrative burden and support more precise, equitable care for acutely ill and injured patients.
KW - Algorithms
KW - critical care
KW - documentation
KW - education
UR - https://www.scopus.com/pages/publications/105034226108
U2 - 10.1136/tsaco-2025-002205
DO - 10.1136/tsaco-2025-002205
M3 - Review article
AN - SCOPUS:105034226108
SN - 2397-5776
VL - 11
JO - Trauma Surgery and Acute Care Open
JF - Trauma Surgery and Acute Care Open
IS - 1
ER -