Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Why We Need Patients and Community at the Center of AI Health Communication Research
0
Zitationen
3
Autoren
2026
Jahr
Abstract
Unlabelled: Holderried and colleagues tested whether artificial intelligence (AI)-generated, patient-centered information can help people understand what they need to do after being discharged from a hospital. Participants demonstrated stronger comprehension when they viewed the simplified, patient-centered information rather than a standard letter. This work adds to the available early-phase evidence of AI supporting hospital discharge communication. To meaningfully progress this area of research, we now need to carefully consider how to enhance the design and evaluation of patient-facing AI health communication tools. In this commentary, we argue that equity and consumer engagement remain underrepresented in studies on patient-facing AI health communication tools and describe possible approaches to address this issue.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.740 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.649 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.202 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.886 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.