Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis ; volume:4 ; number:1 ; day:11 ; month:4 ; year:2024 ; pages:1-10 ; date:12.2024
Communications medicine ; 4, Heft 1 (11.4.2024), 1-10, 12.2024
- Urheber
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s43856-024-00492-0
- URN
-
urn:nbn:de:101:1-2406251037113.166514249424
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:44 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Kolbinger, Fiona
- Veldhuizen, Gregory P.
- Zhu, Jiefu
- Truhn, Daniel
- Kather, Jakob Nikolas
- SpringerLink (Online service)
Ähnliche Objekte (12)
Characterization of pathological angiogenesis in the KLEIP knockout mouse model
Artificial intelligence to identify genetic alterations in conventional histopathology
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
A systematic analysis of deep learning in genomics and histopathology for precision oncology
Deep learning in cancer genomics and histopathology
The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups
Navigating the European Union Artificial Intelligence Act for Healthcare
Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping
The ecological footprint of medical AI
The ecological footprint of medical AI
Collaborative training of medical artificial intelligence models with non-uniform labels
Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy
Characterization of pathological angiogenesis in the KLEIP knockout mouse model
Artificial intelligence to identify genetic alterations in conventional histopathology
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
A systematic analysis of deep learning in genomics and histopathology for precision oncology
Deep learning in cancer genomics and histopathology
The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups
Navigating the European Union Artificial Intelligence Act for Healthcare
Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping
The ecological footprint of medical AI
The ecological footprint of medical AI
Collaborative training of medical artificial intelligence models with non-uniform labels
Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy
Characterization of pathological angiogenesis in the KLEIP knockout mouse model
Artificial intelligence to identify genetic alterations in conventional histopathology
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
A systematic analysis of deep learning in genomics and histopathology for precision oncology
Deep learning in cancer genomics and histopathology
The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups
Navigating the European Union Artificial Intelligence Act for Healthcare
Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping
The ecological footprint of medical AI
The ecological footprint of medical AI
Collaborative training of medical artificial intelligence models with non-uniform labels