Resumen de Is generative artificial intelligence the next step toward a personalized hemodialysis?

Miguel Hueso Val, Rafael Álvarez, David Marí, Vicent Ribas Ripoll, Karim Lekadir, Alfredo Vellido

• 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. (REV INVEST CLIN. 2023;75(6):309-17)