Ethical dimensions associated with the use of machine learning algorithms in surgery
Keywords:
machine learning, ethics in surgery, artificial intelligenceAbstract
Introduction: The decision-making process of surgeons in contemporary practice has changed with the incorporation of advanced technologies, such as machine learning algorithms. These technologies can predict surgical outcomes through the analysis of large volumes of data and contribute to risk reduction. However, their use poses ethical challenges that require careful analysis.
Objective: To conduct a critical analysis of the ethical and moral dimensions associated with the implementation of machine learning algorithms in contemporary surgical practice.
Methods: A review of original research articles, review articles, and systematic reviews published during the last five years was conducted using PubMed, Scopus, and SciELO. Studies addressing the integration of machine learning into medical decision-making, particularly in surgery, as well as those addressing the ethical aspects of its application, were included. Google Scholar and Semantic Scholar were also consulted.
Development: The incorporation of machine learning into surgery offers benefits in predicting complications and supporting decision-making, but poses challenges related to autonomy, transparency, privacy, non-maleficence, beneficence, and justice. Risks associated with algorithmic opacity, bias, technological dependence, unequal access, and accountability for errors were identified. Its implementation requires system validation, human oversight, data protection, and multidisciplinary governance mechanisms.
Conclusions: The principles of medical ethics provide a robust conceptual framework for implementing these technologies.
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