Sousa, Pedro Henrique Rodrigues de2025-04-072025-04-072025KÜMPEL, Altiery. Aceitação de inteligência artificial na prática médica: um estudo em uma rede privada de hospitais no Brasil. Goiânia (GO), 2025. 81 f Dissertação (Mestrado) - Programa de Pós-Graduação em Administração, Centro Universitário Alves Faria (UNIALFA) Goiânia, 2025https://repositorio.unialfa.com.br/handle/123456789/576Artificial intelligence (AI) has emerged as a disruptive technology in the healthcare sector, with the potential to enhance efficiency, diagnostic accuracy, and the quality of medical services. However, the adoption of AI-based tools in clinical practice faces significant challenges, such as technological barriers, resistance from some healthcare professionals, and insufficient specialized training. In this context, understanding the factors influencing AI acceptance among physicians is essential, particularly in private networks of hospitals, where technological infrastructure and professional dynamics present distinct characteristics. This study aimed to analyze AI acceptance in medical practice within a private network of hospitals in Brazil, based on the Technology Acceptance Model (TAM). The specific objectives of this study were: (1) to characterize the demographic and professional profile of the participating physicians, (2) to analyze the influence of demographic variables on Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) within the TAM framework, and (3) to investigate the relationships between PU, PEOU, and other TAM factors (attitude, behavioral intention, and actual use), considering AI acceptance behavior in medical practice. A quantitative approach was adopted, utilizing a structured questionnaire applied to 256 physicians. The instrument was based on a five-point Likert scale to measure the theoretical dimensions of TAM. Data analysis was conducted using descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM), allowing for the evaluation of relationships among the model’s constructs. The results indicated that PU had a strong influence on BI, which was a significant predictor of AU. PEOU impacted PU and ATU, confirming that ease of use contributes to PU and the formation of favorable attitudes toward AI. Age, academic background, professional experience, and medical specialty had no significant effect on PU and PEOU, suggesting that AI acceptance is more influenced by subjective perceptions of the technology than by individual characteristics. Gender had a statistically significant effect on PU and PEOU, but with limited practical relevance. Based on the findings of this study, PU, PEOU, and BI emerged as the main factors associated with the acceptance of AI in medical practice, providing supportive evidence for the applicability of the TAM model in the investigated healthcare context. The results indicate that AI adoption in medical practice can be enhanced through continuous training, adequate technical support, and the efficient integration of technology into clinical workflows. The adjusted model presented in this study, based on structural equation modeling, maintained the theoretical consistency of TAM while refining its structure by eliminating statistically insignificant relationships and proposing a more concise and robust model. Thus, this research contributes to the advancement of studies on technology acceptance in medicine and can serve as a foundation for future investigations aimed at understanding and optimizing AI adoption in clinical settings.ptInteligência ArtificialModelo de Aceitação de Tecnologia (TAM)Facilidade de Uso Percebida (PEOU)Assistência a saúde