Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence and Surgery: Ethical Dilemmas and Open Issues
94
Zitationen
14
Autoren
2022
Jahr
Abstract
BACKGROUND: Artificial intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threats, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial Intelligence framework recently promoted by the European Commission. STUDY DESIGN: A modified Delphi process has been employed to achieve expert consensus. RESULTS: The main ethical issues that arise from applying AI to surgery, described in detail here, relate to human agency, accountability for errors, technical robustness, privacy and data governance, transparency, diversity, non-discrimination, and fairness. It may be possible to address many of these ethical issues by expanding the breadth of surgical AI research to focus on implementation science. The potential for AI to disrupt surgical practice suggests that formal digital health education is becoming increasingly important for surgeons and surgical trainees. CONCLUSIONS: A multidisciplinary focus on implementation science and digital health education is desirable to balance opportunities offered by emerging AI technologies and respect for the ethical principles of a patient-centric philosophy.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.539 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.426 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.921 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.586 Zit.
Autoren
Institutionen
- University of Chicago(US)
- University of Pavia(IT)
- University of Florida Health(US)
- Policlinico San Matteo Fondazione(IT)
- McGill University(CA)
- Istituti di Ricovero e Cura a Carattere Scientifico(IT)
- Université de Strasbourg(FR)
- Ospedale Santa Maria della Misericordia di Udine(IT)
- Ca' Foscari University of Venice(IT)
- Agostino Gemelli University Polyclinic(IT)
- McGill University Health Centre(CA)
- Massachusetts General Hospital(US)