Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The role of machine learning in clinical research: transforming the future of evidence generation
271
Zitationen
21
Autoren
2021
Jahr
Abstract
ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.336 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.207 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.607 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 · 5.476 Zit.
Autoren
- E. Hope Weissler
- Tristan Naumann
- Tomas Andersson
- Rajesh Ranganath
- Olivier Elemento
- Yuan Luo
- Daniel F. Freitag
- James Benoit
- Michael C. Hughes
- Faisal M. Khan
- Paul Slater
- Khader Shameer
- Matthew T. Roe
- Emmette R. Hutchison
- Scott H. Kollins
- Uli C. Broedl
- Zhaoling Meng
- Jennifer Wong
- Lesley H. Curtis
- Erich Huang
- Marzyeh Ghassemi
Institutionen
- Duke University(US)
- Clinical Research Institute(US)
- Duke Medical Center(US)
- Microsoft (United States)(US)
- AstraZeneca (Sweden)(SE)
- Courant Institute of Mathematical Sciences(US)
- New York University(US)
- Cornell University(US)
- Northwestern University(US)
- Bayer (Germany)(DE)
- University of Alberta(CA)
- Tufts University(US)
- Boehringer Ingelheim (Canada)(CA)
- Sanofi (United States)(US)
- University of Toronto(CA)
- Vector Institute(CA)
- Massachusetts Institute of Technology(US)