Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Time to start using checklists for reporting artificial intelligence in health care and biomedical research: a rapid review of available tools
10
Zitationen
3
Autoren
2022
Jahr
Abstract
While the volume of using artificial intelligence (AI) and machine learning (ML) in medical research has grown considerable over the past years, the reporting quality for the majority of such studies has been poor, raising concerns about the replicability, biasedness, validity and overall value for a vast amount of research. This rapid review aims to summarize reporting guidelines for medical AI studies. Following a systematic search in the PubMed database up to May 2022 and the reference lists of previously published reviews in the field, we identified 22 reporting checklists published or under development for a variety of study designs and clinical fields or general use. The main aims, the target audience and specific focus of the identified checklists has been summarized. Given the documented positive impact of checklists on the reporting quality of medical research, we encourage researchers using AI or ML in medicine to start using them.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 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 · 5.428 Zit.