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Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation.
Schwartz, Ilan S; Link, Katherine E; Daneshjou, Roxana; Cortés-Penfield, Nicolás.
Afiliación
  • Schwartz IS; Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
  • Link KE; Department of Medical Education, Icahn School of Medicine at Mount Sinai, NewYork, New York, USA.
  • Daneshjou R; Healthcare & Life Sciences Division, Hugging Face, Brooklyn, NewYork, USA.
  • Cortés-Penfield N; Department of Dermatology, Stanford School of Medicine, Stanford, California, USA.
Clin Infect Dis ; 78(4): 860-866, 2024 Apr 10.
Article en En | MEDLINE | ID: mdl-37971399
Large language models (LLMs) are artificial intelligence systems trained by deep learning algorithms to process natural language and generate text responses to user prompts. Some approach physician performance on a range of medical challenges, leading some proponents to advocate for their potential use in clinical consultation and prompting some consternation about the future of cognitive specialties. However, LLMs currently have limitations that preclude safe clinical deployment in performing specialist consultations, including frequent confabulations, lack of contextual awareness crucial for nuanced diagnostic and treatment plans, inscrutable and unexplainable training data and methods, and propensity to recapitulate biases. Nonetheless, considering the rapid improvement in this technology, growing calls for clinical integration, and healthcare systems that chronically undervalue cognitive specialties, it is critical that infectious diseases clinicians engage with LLMs to enable informed advocacy for how they should-and shouldn't-be used to augment specialist care.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Transmisibles / Etiquetado de Medicamentos Límite: Humans Idioma: En Revista: Clin Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Transmisibles / Etiquetado de Medicamentos Límite: Humans Idioma: En Revista: Clin Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos