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Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists
24
Zitationen
22
Autoren
2023
Jahr
Abstract
Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.
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Autoren
- Matthew G. Hanna
- Niels Olson
- Mark D. Zarella
- Rajesh Dash
- Markus D. Herrmann
- Larissa V. Furtado
- Michelle Stram
- Patricia M. Raciti
- Lewis Hassell
- Alex Mays
- Liron Pantanowitz
- Joseph S. Sirintrapun
- Savitri Krishnamurthy
- Anil V. Parwani
- Giovanni Lujan
- Andrew Evans
- Eric F. Glassy
- Marilyn M. Bui
- Vikram Singh
- Rhona J. Souers
- Monica E. de Baca
- Jansen N. Seheult
Institutionen
- Memorial Sloan Kettering Cancer Center(US)
- Uniformed Services University of the Health Sciences(US)
- Mayo Clinic(US)
- Duke University Health System(US)
- Harvard University(US)
- Massachusetts General Hospital(US)
- St. Jude Children's Research Hospital(US)
- Office of Chief Medical Examiner(US)
- New York University(US)
- OU Health
- University of Oklahoma Health Sciences Center(US)
- Mitre (United States)(US)
- University of Michigan(US)
- The University of Texas MD Anderson Cancer Center(US)
- The Ohio State University Wexner Medical Center(US)
- Cardiovascular Medical Group(US)
- Moffitt Cancer Center(US)
- College of American Pathologists(US)
- Seattle Pacific University(US)