World journal of surgery · 2025
Ethical Pitfalls in AI-Based Predictive Models in Surgery.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Ben Hmido Sara, Abder Rahim Houssam, Keller Boris, Daams Freek, Schakel Matthijs, Goslings J Carel, Nieveen van Dijkum E J M, Rainey Stephen, Kazemier Geert, Bak Marieke, Ploem Corrette
Аннотация
BACKGROUND: Predictive models in surgery promise to improve clinical care by anticipating complications, guiding decision-making, and supporting personalized treatment strategies. Although their potential to enhance outcomes and efficiency is substantial, their integration into clinical practice also raises profound ethical challenges.
ETHICAL FRAMEWORK: These challenges span the entire lifecycle of predictive models from data collection and development to validation and clinical use. They touch upon patient privacy, algorithmic bias, transparency, and the shifting responsibilities of clinicians. Importantly, the ethical concerns are not isolated to one group but shared across patients, developers, and clinicians within a dynamic stakeholder relationship.
ANALYSIS: Key risks include biased or unrepresentative datasets, privacy breaches, opaque decision-making processes, and the danger of deskilling surgeons if reliance on algorithms becomes excessive. To mitigate these risks, strategies, such as out-of-distribution detection, standardized data collection, parallel model development, and continuous auditing, are essential. Beyond technical safeguards, embedding predictive models within a framework of accountability and patient-centered care is necessary to sustain trust and equity.
CONCLUSION: The integration of predictive models into surgery requires more than technical excellence, and it demands ethical vigilance. Preparing future clinicians through education that emphasizes both clinical reasoning and ethical awareness is critical. By aligning predictive model development with human-centered values, healthcare systems can ensure that these innovations enhance surgical practice while safeguarding equity, transparency, and patient trust.
ETHICAL FRAMEWORK: These challenges span the entire lifecycle of predictive models from data collection and development to validation and clinical use. They touch upon patient privacy, algorithmic bias, transparency, and the shifting responsibilities of clinicians. Importantly, the ethical concerns are not isolated to one group but shared across patients, developers, and clinicians within a dynamic stakeholder relationship.
ANALYSIS: Key risks include biased or unrepresentative datasets, privacy breaches, opaque decision-making processes, and the danger of deskilling surgeons if reliance on algorithms becomes excessive. To mitigate these risks, strategies, such as out-of-distribution detection, standardized data collection, parallel model development, and continuous auditing, are essential. Beyond technical safeguards, embedding predictive models within a framework of accountability and patient-centered care is necessary to sustain trust and equity.
CONCLUSION: The integration of predictive models into surgery requires more than technical excellence, and it demands ethical vigilance. Preparing future clinicians through education that emphasizes both clinical reasoning and ethical awareness is critical. By aligning predictive model development with human-centered values, healthcare systems can ensure that these innovations enhance surgical practice while safeguarding equity, transparency, and patient trust.