Ethical Pitfalls in AI-Based Predictive Models in Surgery.

Area of Science:

• Surgical Innovation
• Medical Ethics
• Artificial Intelligence in Healthcare

Background:

• Predictive models in surgery aim to enhance clinical care by anticipating complications and personalizing treatments.
• Despite potential benefits, integrating these models into practice introduces significant ethical challenges.
• Ethical concerns span the entire model lifecycle, from data collection to clinical application, affecting all stakeholders.

Purpose of the Study:

• To identify and analyze the ethical challenges associated with predictive models in surgery.
• To explore risks such as data bias, privacy breaches, and algorithmic opacity.
• To propose mitigation strategies and an ethical framework for responsible integration.

Main Methods:

• Review of ethical considerations throughout the predictive model lifecycle in surgery.
• Analysis of risks including dataset bias, privacy violations, and lack of transparency.
• Identification of mitigation strategies like out-of-distribution detection and continuous auditing.

Main Results:

• Key risks identified: biased datasets, privacy breaches, opaque decision-making, and potential surgeon deskilling.
• Essential mitigation strategies include standardized data collection, parallel development, and auditing.
• An ethical framework emphasizing accountability and patient-centered care is vital.

Conclusions:

• Integrating predictive models in surgery requires technical expertise and ethical vigilance.
• Education for future clinicians must include clinical reasoning and ethical awareness.
• Aligning AI development with human-centered values ensures enhanced surgical practice while upholding equity and trust.