ORTHOPEDIC SURGEONS AND GENERATIVE AI: NAVIGATING THE PERCEPTION-ADOPTION GAP AMIDST RAPIDLY ADVANCING DIGITAL TECHNOLOGIES IN HEALTHCARE

The rapid advancement of generative artificial intelligence (AI) technologies, particularly large language models (LLMs), is poised to revolutionize the healthcare sector. This dissertation explores the complex landscape of generative AI adoption in orthopedic surgery, focusing on the perceptions and intentions of orthopedic surgeons, who play a pivotal role in determining the success and feasibility of AI integration in high-stakes environments. This study investigated factors influencing surgeons ' adoption of generative AI tools in their practice through a mixed methods approach grounded in the unified theory of acceptance and use of technology (UTAUT) and social cognitive theory. The research employs a quantitative survey of 177 orthopedic surgeons complemented by qualitative follow-up interviews to understand their familiarity with generative AI, the intrinsic and extrinsic factors shaping their views, and their beliefs about AI's role in augmenting or replacing human expertise. The findings reveal that, whereas orthopedic surgeons hold positive attitudes about the transformative potential of generative AI, their behavioral intentions and self-reported actual use remain in the early stages. Performance expectancy and facilitating conditions emerge as key determinants of adoption, underscoring the importance of perceived benefits and organizational support. Interestingly, the appeal of generative AI may differ based on the surgeon's career stage, with expert, later-career surgeons being drawn to its potential for streamlining their work and making their jobs easier. However, the study also highlights significant barriers to adoption, including systemic and institutional challenges, technological limitations, and concerns surrounding patient trust and ethics. The qualitative insights provide an indicative but nuanced understanding of surgeons' experiences and decision-making processes, revealing a cautious optimism tempered by the realities of integrating early-stage technologies into clinical practice.This dissertation contributes to the growing body of literature on AI adoption in healthcare, offering valuable insights for orthopedic surgeons navigating the integration of generative AI in their practice. The findings reveal that while surgeons hold positive attitudes about AI's potential, actual adoption remains in the early stages, with higher usage in clinical research and professional development than in direct patient care. Performance expectancy and facilitating conditions emerge as key determinants of adoption, underscoring the importance of perceived benefits and organizational support. Notably, the appeal of generative AI may differ based on career stage, with experienced surgeons viewing it as a means to streamline workflows. The study emphasizes the need for a collaborative approach to address challenges such as workflow integration, trust-building, and ethical concerns, ensuring that generative AI enhances rather than replaces the irreplaceable human touch in healthcare. By providing a strategic roadmap for navigating the early stages of AI adoption, this research paves the way for a future wherein surgeons and machines work together to improve surgical outcomes and transform patient care.