Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective

被引:1
作者
De Simone, Belinda [1 ,2 ]
Deeken, Genevieve [3 ]
Catena, Fausto [4 ,5 ]
机构
[1] AUSL Romagna, Infermi Hosp, Dept Emergency & Digest Minimally Invas Surg, I-47921 Romagna, Rimini, Italy
[2] Campus Univ, Dept Theoret & Appl Sci, I-22060 Novedrate, Como, Italy
[3] Univ Virginia, Dept Global Publ Hlth Global Studies, Charlottesville, VA 22903 USA
[4] Bufalini Hosp, Trauma Ctr, Dept Emergency & Gen Surg, Level 1, I-474521 Cesena, Italy
[5] Univ Bologna, Alma Mater Studiorum, I-40100 Bologna, Italy
关键词
artificial intelligence; machine learning; ethics; surgery; emergency surgery; healthcare; transparency; accountability; black box effect; BLACK-BOX; CARE;
D O I
10.3390/jcm14093111
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Artificial intelligence (AI) is increasingly shaping the landscape of emergency surgery by offering real-time decision support, enhancing diagnostic accuracy, and optimizing workflows. However, its implementation raises significant ethical concerns, particularly regarding accountability, transparency, patient autonomy, and bias. Objective: This perspective paper, grounded in a narrative review, explores the ethical dilemmas associated with AI in emergency surgery and proposes future directions for its responsible and equitable integration. Methods: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and Google Scholar, covering the literature published from January 2010 to December 2024. We focused on peer-reviewed articles discussing AI in surgical or emergency care and highlighting ethical, legal, or regulatory issues. A thematic analysis was used to synthesize the main ethical challenges. Results: Key ethical concerns identified include issues of accountability in AI-assisted decision-making, the "black box" effect and bias in algorithmic design, data privacy and protection, and the lack of global regulatory coherence. Thematic domains were developed around autonomy, beneficence, justice, transparency, and informed consent. Conclusions: Responsible AI implementation in emergency surgery requires transparent and explainable models, diverse and representative datasets, robust consent frameworks, and clear guidelines for liability and oversight. Interdisciplinary collaboration is essential to align technological innovation with patient-centered and ethically sound clinical practice.
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页数:18
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