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Is generative artificial intelligence the next step toward a personalized hemodialysis?
Hueso, Miguel; Álvarez, Rafael; Marí, David; Ribas-Ripoll, Vicent; Lekadir, Karim; Vellido, Alfredo.
Afiliação
  • Hueso M; Department of Nephrology, Hospital Universitari Bellvitge and Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain.
  • Álvarez R; BigData and Artificial Intelligence Group (BigSEN Working Group), Spanish Society of Nephrology (SENEFRO), Santander, España.
  • Marí D; Department of Nephrology, Hospital Universitari Bellvitge and Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, Barcelona, Spain.
  • Ribas-Ripoll V; Digital Health Unit, Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain.
  • Lekadir K; Digital Health Unit, Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain.
  • Vellido A; Artificial Intelligence in Medicine Lab (BCN-AIM), Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain.
Rev Invest Clin ; 75(6): 309-317, 2023 12 18.
Article em En | MEDLINE | ID: mdl-37734067
Artificial intelligence (AI) generative models driven by the integration of AI and natural language processing technologies, such as OpenAI's chatbot generative pre-trained transformer large language model (LLM), are receiving much public attention and have the potential to transform personalized medicine. Dialysis patients are highly dependent on technology and their treatment generates a challenging large volume of data that has to be analyzed for knowledge extraction. We argue that, by integrating the data acquired from hemodialysis treatments with the powerful conversational capabilities of LLMs, nephrologists could personalize treatments adapted to patients' lifestyles and preferences. We also argue that this new conversational AI integrated with a personalized patient-computer interface will enhance patients' engagement and self-care by providing them with a more personalized experience. However, generative AI models require continuous and accurate updates of data, and expert supervision and must address potential biases and limitations. Dialysis patients can also benefit from other new emerging technologies such as Digital Twins with which patients' care can also be addressed from a personalized medicine perspective. In this paper, we will revise LLMs potential strengths in terms of their contribution to personalized medicine, and, in particular, their potential impact, and limitations in nephrology. Nephrologists' collaboration with AI academia and companies, to develop algorithms and models that are more transparent, understandable, and trustworthy, will be crucial for the next generation of dialysis patients. The combination of technology, patient-specific data, and AI should contribute to create a more personalized and interactive dialysis process, improving patients' quality of life.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Qualidade de Vida / Inteligência Artificial Aspecto: Patient_preference Limite: Humans Idioma: En Revista: Rev Invest Clin Assunto da revista: MEDICINA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha País de publicação: México
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Qualidade de Vida / Inteligência Artificial Aspecto: Patient_preference Limite: Humans Idioma: En Revista: Rev Invest Clin Assunto da revista: MEDICINA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha País de publicação: México