<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0034-8376</journal-id>
<journal-title><![CDATA[Revista de investigación clínica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. invest. clín.]]></abbrev-journal-title>
<issn>0034-8376</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0034-83762023000600309</article-id>
<article-id pub-id-type="doi">10.24875/ric.23000162</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Is Generative Artificial Intelligence the Next Step Toward a Personalized Hemodialysis?]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hueso]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Álvarez]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marí]]></surname>
<given-names><![CDATA[David]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ribas-Ripoll]]></surname>
<given-names><![CDATA[Vicent]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lekadir]]></surname>
<given-names><![CDATA[Karim]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vellido]]></surname>
<given-names><![CDATA[Alfredo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Institut d'Investigació Biomèdica de Bellvitge  ]]></institution>
<addr-line><![CDATA[Barcelona ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Spanish Society of Nephrology BigData and Artificial Intelligence Group ]]></institution>
<addr-line><![CDATA[Santander ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Eurecat Centro Tecnológico de Cataluña Digital Health Unit ]]></institution>
<addr-line><![CDATA[Barcelona ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,University of Barcelona Department of Mathematics and Computer Science Artificial Intelligence in Medicine Lab]]></institution>
<addr-line><![CDATA[Barcelona ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universitat Politècnica de Catalunya Intelligent Data Science and Artificial Intelligence Research Center ]]></institution>
<addr-line><![CDATA[Barcelona ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,Centro de Investigación Biomédica en Red  ]]></institution>
<addr-line><![CDATA[Barcelona ]]></addr-line>
<country>Spain</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>75</volume>
<numero>6</numero>
<fpage>309</fpage>
<lpage>317</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0034-83762023000600309&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0034-83762023000600309&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0034-83762023000600309&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT 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.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Personalized hemodialysis]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Natural language processing]]></kwd>
<kwd lng="en"><![CDATA[Large Language Models]]></kwd>
</kwd-group>
</article-meta>
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