Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A Practical and Prescriptive Framework for Appropriate Implementation and Review of Artificial Intelligence (FAIR-AI) in Healthcare
1
Zitationen
14
Autoren
2025
Jahr
Abstract
<title>Abstract</title> Health systems face the challenge of balancing innovation and safety to responsibly implement artificial intelligence (AI) solutions. The rapid proliferation, growing complexity, ethical considerations, and rising demand for these tools require timely and efficient processes for rigorous evaluation and ongoing monitoring. Current AI evaluation frameworks often lack the practical guidance for health systems to address these challenges. To fill this gap, we developed a prescriptive evaluation and governance framework informed by a literature review, in-depth interviews with key stakeholders, including patients, and a multidisciplinary design workshop. The resulting framework provides health systems an outline of the resources, structures, criteria, and template documents to enable pre-implementation evaluation and post-implementation monitoring of AI solutions. Health systems will need to treat this or any alternative framework as a living document to maintain relevance and effectiveness as the AI landscape and regulations continue to evolve.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.200 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.051 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.416 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.410 Zit.