Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Facilitating a Shared Meaning of AI/ML Findings amongst Key Healthcare Stakeholders
0
Zitationen
3
Autoren
2023
Jahr
Abstract
Analytics, artificial intelligence (AI) and machine learning (ML) have demonstrated their benefits in various industries including banking and finance, and manufacturing. Traditionally, healthcare has been slow to embrace technological interventions; however, we are now witnessing the rapid adoption and deployment of a myriad of AI/ML and analytic techniques in various healthcare contexts. Experts are noting that the embracement of AI/ML and analytics in clinical decision-making will be a key differentiator in the provision of optimal care. In such a context, we believe a critical success factor is the need for all stakeholders to have the same understanding and shared meaning. In this chapter, we address this goal by suggesting a role for analytic translators and explaining how translators facilitate mutual understanding.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.635 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.543 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.051 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.844 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.