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ChatGPT for digital pathology research.
Omar, Mohamed; Ullanat, Varun; Loda, Massimo; Marchionni, Luigi; Umeton, Renato.
Afiliación
  • Omar M; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Ullanat V; Department of Informatics & Analytics, Dana Farber Cancer Institute, Boston, MA, USA.
  • Loda M; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; Department of Informatics & Analytics, Dana Farber Cancer Institute, Boston, MA, USA.
  • Marchionni L; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; Department of Informatics & Analytics, Dana Farber Cancer Institute, Boston, MA, USA.
  • Umeton R; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; Department of Informatics & Analytics, Dana Farber Cancer Institute, Boston, MA, USA. Electronic address: renato_umeton@dfci.harvard.edu.
Lancet Digit Health ; 6(8): e595-e600, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38987117
ABSTRACT
The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discuss how domain-specific approaches substantially minimise the risk of inaccurate responses, enhancing the reliability and accuracy of information extraction. We also highlight the broader implications of such tools, particularly in streamlining access to scientific research and democratising access to computational pathology techniques for scientists with little coding experience. This Viewpoint calls for an enhanced integration of domain-specific text-generation AI tools in academic settings to facilitate continuous learning and adaptation to the dynamically evolving landscape of medical research.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial Límite: Humans Idioma: En Revista: Lancet Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial Límite: Humans Idioma: En Revista: Lancet Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido