Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
SORS: best practices for trustworthy and ethical AI in biomedicine
0
Zitationen
1
Autoren
2025
Jahr
Abstract
Despite significant advances in artificial intelligence (AI) for healthcare, its applicability remains hindered by significant challenges in real-world practice. These include limited trust and ethical risks, such as generalisability issues, biases, lack of transparency, potential errors, and safety concerns. To encourage the acceptance and adoption of new AI tools, it is crucial to establish best practices that ensure the development of trustworthy and ethical AI tools for healthcare practice. This talk will introduce FUTURE-AI, a code of practice developed by an international consortium of 117 experts from 50 countries to promote AI tools that earn the trust and acceptance of patients, clinicians, health organisations, and regulatory authorities. The FUTURE-AI guideline advocates for Fair, Universal, Traceable, Usable, Robust, and Explainable AI solutions. It provides step-by-step guidance with actionable recommendations that span the entire AI production lifecycle, from design and development to validation and deployment. The talk will include practical examples from diverse fields such as oncology, cardiology, and women’s health to demonstrate the guideline’s promise in various healthcare contexts.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.521 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.412 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.891 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 · 5.575 Zit.