Bias in medizinischen KI-Systemen: Entstehung, Risiken und medizinethische Perspektiven = Bias in Medical AI Systems: Origins, Risks, and Ethical Perspectives
AbstractArtificial intelligence is rapidly transforming medicine by enabling novel forms of precision, efficiency, and personalization. At the same time, algorithmic bias emerges as a critical challenge, shaping diagnostic accuracy, treatment decisions, and equity of care. Bias arises from imbalance...
| Subtitles: | Bias in Medical AI Systems: Origins, Risks, and Ethical Perspectives Medizinische Nutzung von Künstlicher Intelligenz (KI) |
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| Main Author: | |
| Format: | Electronic Article |
| Language: | German |
| Check availability: | HBZ Gateway |
| Interlibrary Loan: | Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany) |
| Published: |
2025
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| In: |
Zeitschrift für medizinische Ethik
Year: 2025, Volume: 71, Issue: 4, Pages: 469-487 |
| Further subjects: | B
algorithmic discrimination
B Artificial Intelligence B Medical Ethics B Health equity B Algorithmische Diskriminierung B Medizinethik B Test bias B Artificial intelligence |
| Online Access: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Summary: | AbstractArtificial intelligence is rapidly transforming medicine by enabling novel forms of precision, efficiency, and personalization. At the same time, algorithmic bias emerges as a critical challenge, shaping diagnostic accuracy, treatment decisions, and equity of care. Bias arises from imbalanced datasets, proxy-driven modeling, and context-sensitive deployment, and can amplify existing health disparities. These dynamics reveal that bias in medical AI is not only a technical shortcoming but also an ethical and societal concern., AbstractArtificial intelligence is rapidly transforming medicine by enabling novel forms of precision, efficiency, and personalization. At the same time, algorithmic bias emerges as a critical challenge, shaping diagnostic accuracy, treatment decisions, and equity of care. Bias arises from imbalanced datasets, proxy-driven modeling, and context-sensitive deployment, and can amplify existing health disparities. These dynamics reveal that bias in medical AI is not only a technical shortcoming but also an ethical and societal concern. Künstliche Intelligenz (KI) bietet große Chancen für Präzision und Effizienz im Gesundheitswesen, birgt jedoch auch gewisse Risiken wie algorithmische Verzerrungen (Bias). Diese können durch unausgewogene Trainingsdaten, Fehlentscheidungen im Rahmen der Modellierung sowie in der kontextabhängigen Anwendung entstehen und zu einer negativen Ungleichbehandlung und Verstärkung bestehender gesundheitlicher Disparitäten führen. Bias in medizinischen KI-Anwendungen stellt damit nicht nur ein technisches, sondern auch ein ethisches Problem dar. |
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| ISSN: | 2949-8570 |
| Contains: | Enthalten in: Zeitschrift für medizinische Ethik
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| Persistent identifiers: | DOI: 10.30965/29498570-20250142 |
