Artificial Intelligence in Experimental Surgery: Ethical Breakthroughs and Technological Innovations within In Silico Models

Abstract

Integrating artificial intelligence (AI) into experimental surgery represents a transformative shift in biomedical research, offering innovative alternatives to traditional animal-based preclinical models. AI-driven methodologies, including computerized models and surgical simulations, enhance precision, reproducibility, and ethical compliance while reducing reliance on _in vivo_ experimentation. This review systematically explores the role of AI in optimizing surgical procedures, operative techniques, and biomedical technology, analyzing its impact on surgical decision-making, predictive modeling, and training simulations. A comprehensive search was conducted across PubMed, Embase, Scopus, Web of Science, and SciELO, identifying studies on AI-enhanced surgical strategies, in silico models, and experimental validation techniques. The findings highlight AI's potential to replace animal testing, refine surgical training, and improve preclinical research accuracy. However, challenges remain, including data standardization, regulatory adaptation, and ethical considerations related to AI-driven surgical methodologies. Addressing these challenges requires interdisciplinary collaboration and the development of validated AI frameworks to support widespread implementation in experimental surgery. Future research should focus on standardizing AI applications, ensuring methodological transparency, and integrating AI models into clinical translation pathways. This review underscores AI's revolutionary role in shaping the future of surgical research, offering a path to more ethical, precise, and innovative experimental surgery.

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