Die kommende Entwicklung großer Sprachmodelle in der Medizin Article Swipe
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1159/000536600
Große Sprachmodelle (Large Language Models, LLMs) sind Tools der künstlichen Intelligenz (KI), die speziell für die Verarbeitung und Erzeugung von Text trainiert sind. LLMs erregten erhebliche öffentliche Aufmerksamkeit, nachdem ChatGPT von OpenAI im November 2022 öffentlich zugänglich gemacht wurde. LLMs können Fragen beantworten, Texte zusammenfassen, paraphrasieren und übersetzen, und zwar auf einer Ebene, die von menschlichen Fähigkeiten kaum zu unterscheiden ist. Die Möglichkeit, aktiv mit Modellen wie ChatGPT zu interagieren, macht LLMs zu attraktiven Tools in verschiedenen Bereichen, einschließlich der Medizin. Diese Modelle haben zwar das Potenzial, medizinisches Wissen zu demokratisieren und den Zugang zur Gesundheitsversorgung zu erleichtern, sie könnten jedoch aufgrund mangelnder Rechenschaftspflicht und Transparenz ebenso Fehlinformationen verbreiten und wissenschaftliches Fehlverhalten verschlimmern. In diesem Artikel geben wir einen systematischen und umfassenden Überblick über die Potenziale und Grenzen von LLMs in der klinischen Praxis, der medizinischen Forschung und der medizinischen Ausbildung.
Related Topics
- Type
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- Language
- de
- Landing Page
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- https://karger.com/kko/article-pdf/doi/10.1159/000536600/4168273/000536600.pdf
- OA Status
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- OpenAlex ID
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https://doi.org/10.1159/000536600Digital Object Identifier
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Die kommende Entwicklung großer Sprachmodelle in der MedizinWork title
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articleOpenAlex work type
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dePrimary language
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Jan Clusmann, Fiona R. Kolbinger, Hannah Sophie Muti, Zunamys I. Carrero, Jan‐Niklas Eckardt, Narmin Ghaffari Laleh, Chiara Maria Lavinia Löffler, Sophie-Caroline Schwarzkopf, Michaela Unger, Gregory Patrick Veldhuizen, Sophia J. Wagner, Jakob Nikolas KatherList of authors in order
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https://karger.com/kko/article-pdf/doi/10.1159/000536600/4168273/000536600.pdfDirect link to full text PDF
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.beantworten, | 42 |
| abstract_inverted_index.erleichtern, | 97 |
| abstract_inverted_index.künstlichen | 9 |
| abstract_inverted_index.menschlichen | 55 |
| abstract_inverted_index.öffentliche | 26 |
| abstract_inverted_index.übersetzen, | 47 |
| abstract_inverted_index.Fehlverhalten | 111 |
| abstract_inverted_index.Möglichkeit, | 62 |
| abstract_inverted_index.Sprachmodelle | 1 |
| abstract_inverted_index.interagieren, | 69 |
| abstract_inverted_index.medizinischen | 135, 139 |
| abstract_inverted_index.medizinisches | 87 |
| abstract_inverted_index.unterscheiden | 59 |
| abstract_inverted_index.verschiedenen | 76 |
| abstract_inverted_index.paraphrasieren | 45 |
| abstract_inverted_index.systematischen | 119 |
| abstract_inverted_index.verschlimmern. | 112 |
| abstract_inverted_index.Aufmerksamkeit, | 27 |
| abstract_inverted_index.demokratisieren | 90 |
| abstract_inverted_index.einschließlich | 78 |
| abstract_inverted_index.zusammenfassen, | 44 |
| abstract_inverted_index.Fehlinformationen | 107 |
| abstract_inverted_index.wissenschaftliches | 110 |
| abstract_inverted_index.Rechenschaftspflicht | 103 |
| abstract_inverted_index.Gesundheitsversorgung | 95 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.76508662 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |