Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Large language models are less effective at clinical prediction tasks than locally trained machine learning models
2025·25 Zitationen·Journal of the American Medical Informatics AssociationOpen Access
Volltext beim Verlag öffnen25
Zitationen
10
Autoren
2025
Jahr
Abstract
These findings suggest that non-fine-tuned LLMs are less effective and robust than locally trained ML for clinical prediction tasks, but they are improving across releases.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.432 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.749 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.288 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.726 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.449 Zit.
Autoren
Institutionen
Themen
Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationAutopsy Techniques and Outcomes