Evaluating multimodal AI in medical diagnostics
Abstract: This study evaluates multimodal AI models’ accuracy and responsiveness in answering NEJM Image Challenge questions, juxtaposed with human collective intelligence, underscoring AI’s potential and current limitations in clinical diagnostics. Anthropic’s Claude 3 family demonstrated the highest accuracy among the evaluated AI models, surpassing the average human accuracy, while collective human decision-making outperformed all AI models. GPT-4 Vision Preview exhibited selectivity, responding more to easier questions with smaller images and longer questions
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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npj digital medicine. - 7, 1 (2024) , 205, ISSN: 2398-6352
- Klassifikation
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Medizin, Gesundheit
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
-
Universität
- (wann)
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2024
- Urheber
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Kaczmarczyk, Robert
Wilhelm, Theresa Isabelle
Martin, Ron
Roos, Jonas
- DOI
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10.1038/s41746-024-01208-3
- URN
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urn:nbn:de:bsz:25-freidok-2565754
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:50 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
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Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Kaczmarczyk, Robert
- Wilhelm, Theresa Isabelle
- Martin, Ron
- Roos, Jonas
- Universität
Entstanden
- 2024
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