Machine learning for medical error prevention in departments of surgery: A review of challenges and biases

Abstract

Medical errors in surgical departments pose significant risks to patient safety and healthcare efficiency, yet traditional error prevention strategies remain insufficient. While industries like aviation employ systematic approaches to mitigate errors, healthcare has been slower to adopt such measures. Machine learning (ML) offers promising solutions by enhancing decision-making and reducing human error; however, its implementation in surgery is hindered by biases and limitations. This review synthesizes literature on ML applications in surgical error prevention, identifying key challenges: (1) data-related biases (e.g., underrepresentation of minority groups, anatomical bias, and poor data quality); (2) algorithmic limitations (e.g., "black box" opacity, over fitting, and small sample sizes); (3) deployment barriers (e.g., clinician distrust and lack of generalizability); and (4) ethical and legal concerns (e.g., accountability gaps and exacerbation of healthcare disparities). Mitigation strategies, including improved data curation, robust validation, and transparency-enhancing techniques, are discussed to address these issues. Despite ML’s potential, its success depends on overcoming these challenges to ensure equitable, reliable, and clinically actionable tools. This review underscores the need for interdisciplinary collaboration to refine ML models for surgical safety, balancing innovation with ethical responsibility.

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