{"id":5084,"date":"2026-09-25T12:24:32","date_gmt":"2026-09-25T12:24:32","guid":{"rendered":"https:\/\/itaca.ai\/?page_id=5084"},"modified":"2026-10-01T00:57:54","modified_gmt":"2026-10-01T00:57:54","slug":"medical-practice-2026","status":"publish","type":"page","link":"https:\/\/itaca.ai\/en\/informes\/ia-practica-medica-2026\/","title":{"rendered":"Clinical AI enters the workflow: what is changing in medical practice"},"content":{"rendered":"<div class=\"wp-block-group alignfull has-background-background-color has-background is-layout-constrained wp-container-core-group-is-layout-29cee9e6 wp-block-group-is-layout-constrained\" style=\"padding-top:4.5rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:4.5rem;padding-left:var(--wp--preset--spacing--40)\">\n<p class=\"has-primary-color has-text-color has-small-font-size wp-block-paragraph\" style=\"font-style:normal;font-weight:700;letter-spacing:4px;text-transform:uppercase\">REPORT \u00b7 SEPTEMBER 2026<\/p>\n\n\n\n<h1 class=\"wp-block-heading has-heading-color-color has-text-color has-xx-large-font-size\" style=\"margin-top:var(--wp--preset--spacing--medium);margin-bottom:var(--wp--preset--spacing--medium);font-style:normal;font-weight:700;line-height:1.08\">Clinical AI enters the workflow: what is changing in medical practice<\/h1>\n\n\n\n<p class=\"has-foreground-color has-text-color wp-block-paragraph\" style=\"font-size:21px;line-height:1.7\">The conversation about AI in medicine is changing. The question is no longer just what a model can generate; it\u2019s also where it fits into everyday practice, who reviews its results, and what conditions a clinic needs to meet to incorporate it without adding more work.<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color wp-block-paragraph\"><strong>Developed by the Product and Analysis Team of Itaca.<\/strong> This analysis combines scientific evidence, professional studies, regional adoption frameworks, and aggregated usage signals Itaca from January 1 to September 24, 2026. The proprietary data complement public evidence; it is not presented as a measurement of the Latin American market.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-group alignfull has-background-alt-background-color has-background is-layout-constrained wp-container-core-group-is-layout-72de50b2 wp-block-group-is-layout-constrained\" style=\"padding-top:4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:4rem;padding-left:var(--wp--preset--spacing--40)\">\n<h2 class=\"wp-block-heading\">From testing tools to integrating them into practice<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:19px;line-height:1.7\">Adoption is moving from curiosity about a general tool to uses related to specific tasks. In its 2026 survey, <a href=\"https:\/\/www.ama-assn.org\/sites\/ama-assn.org\/files\/2026-03\/physician-ai-sentiment-report_0.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">American Medical Association<\/a> He found that 81 % of the physicians surveyed in the United States declared that they knew or used AI in their practice. Research summaries and care standards, along with documentation and note-taking, appeared among the most widespread uses.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In Latin America and the Caribbean, one <a href=\"https:\/\/www.paho.org\/en\/news\/18-11-2025-artificial-intelligence-and-health-workforce-paho-presents-results-regional-study\" target=\"_blank\" rel=\"noopener noreferrer\">Regional study presented by OPS<\/a> It showed a broad exposure to AI tools among professionals and students, but also a gap in training and barriers related to digital skills, budget, regulation, and interoperability. Both contexts point to the same transition: the challenge no longer lies solely in accessing AI, but in incorporating it with evidence, oversight, and clear processes.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>Before the appointment:<\/strong> Gather context without creating another inbox.<\/li><li><strong>During care:<\/strong> reduce manual capture without displacing the professional from the patient.<\/li><li><strong>After the meeting:<\/strong> converting the revised context into documents and follow-up.<\/li><li><strong>Throughout the flow:<\/strong> adapt the depth of the information search to the clinical question.<\/li><\/ul>\n<\/div>\n\n\n\n<div class=\"wp-block-group alignfull has-background-background-color has-background is-layout-constrained wp-container-core-group-is-layout-a5cbf47a wp-block-group-is-layout-constrained\" style=\"padding-top:4.5rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:4.5rem;padding-left:var(--wp--preset--spacing--40)\">\n<h2 class=\"wp-block-heading\">What the real-world use reveals about the incorporation of AI into clinical work<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:19px;line-height:1.7\">To complement public surveys and frameworks with observed behavior, we analyzed five clinical flows supported by Itaca. The cohort is concentrated in Latin America, but it is not a probabilistic sample and does not allow for estimating regional adoption. Its usefulness is more limited: observing what happens after a first action, what uses are repeated, which outcomes are concentrated, and where coordination introduces friction.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Clinical responses are used as conversation, not just as an isolated consultation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">50.3 % % of completed responses Itaca were follow-up questions, and 33.8% % appeared at least at the second level of a conversation. The median time between completing a response and completing its follow-up was 4.3 minutes; 81.9% % of follow-up responses were completed within the first 30 minutes. In this cohort, the clinical response was frequently used to clarify, narrow, or deepen a question while the topic was still active.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The documentation occurs during the consultation, but habit requires repetition.<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In the documented sessions observed, 79. % 9 were conducted through live recording and 13.9 % through rapid recording. However, initiating the flow does not equate to incorporating it as a habit: among those who completed at least one session, the median was one; 29.9 had % activity on two or more days and 13.8 on % four or more different weeks. Since each user had a different observation window, these figures do not measure retention; they show why a first trial is not enough to affirm sustained adoption.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">deep research it had a more selective use than the quick responses<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Among those who used deep research it, the median was a completed research study and 33.5 % resorted to the function twice or more. The pattern is different from that of brief responses: the extensive review of sources appears as a more selective layer, used when a question warrants greater depth.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Recipes and orders concentrated the generated documents<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Recipes and orders together accounted for 76.9 % % of the documents completed Itaca during the analyzed period. Clinical summaries, forms, referrals, certificates, and other types accounted for the remaining percentage. The pattern shows that the post-consultation use was concentrated on frequent and directly actionable documents.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The questionnaires are completed soon or are never completed.<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Among the questionnaires shared with a full 30-day observation period, 13. % 0 were completed within the first seven days and 14.0 % within thirty. Among those who completed them within that timeframe, the median time to response was 1.7 hours. The small difference between seven and thirty days suggests that waiting longer does not solve the friction on its own: the main challenge lies in turning the invitation into a patient response.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">The data do not describe a linear adoption. AI enters through specific tasks, deepens into brief conversations, and encounters significant friction when the flow depends on patient engagement or forming a new habit.<\/p>\n<\/blockquote>\n\n\n\n<p class=\"has-foreground-color has-text-color has-small-font-size wp-block-paragraph\"><a href=\"#metodologia\">Please consult the methodology, definitions, and limitations of these data.<\/a><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-group alignfull has-background-background-color has-background is-layout-constrained wp-container-core-group-is-layout-5fb33f64 wp-block-group-is-layout-constrained\" style=\"padding-top:5rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:5rem;padding-left:var(--wp--preset--spacing--40)\">\n<h2 class=\"wp-block-heading\">The useful unit is not a function: it is the complete flow<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A consultation does not begin when a recording is activated or end when a note is generated. It begins with background information, documents, questions and previous decisions. It continues with the clinical conversation. It then leaves notes, instructions, orders, references, summaries, and follow-up tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That\u2019s why a tool can seem impressive in a demonstration and still add work in practice. If it forces you to copy information between systems, remember what function to use, or correct results outside the usual flow, the cost of adoption competes with the benefit.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The experience of building Itaca in Latin America points to a simple rule: AI is adopted better when it reduces a transition. The professional should not stop paying attention to \u201ctalking to AI\u201d; they should be able to continue with their work and review a useful result at the appropriate time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Before the consultation: arrive with context, not another inbox<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Pre-planning can consume a significant portion of the meeting: locating background information, reviewing documents, remembering the reason for follow-up, or re-asking routine questions. AI can help organize that material, but only if it is presented in a way that the professional can verify it quickly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The most meaningful uses at this stage are specific: organizing responses from a questionnaire, summarizing documents provided, retrieving the context of previous inquiries, and making pending ones visible. The data Itaca show that sending a questionnaire alone is not enough to incorporate it into the workflow: starting with more context also depends on coordination, the message to the patient, and reminders.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In a clinic, this point also reveals an operational issue: who requests the information, where is it stored, and what actually reaches the professional? Incorporating AI without solving these three questions can digitalize the fragmentation instead of reducing it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">During the consultation: document without turning attention into data capture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Assisted documentation is one of the most visible uses of clinical AI because it connects with a repetitive task in almost every encounter. The predominance of live recording in data sets Itaca places this aid within the realm of care: preparing the draft while the professional is attending and leaving a note ready to be reviewed, rather than reconstructing the consultation from scratch.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The evidence is still growing and should be read with caution. <a href=\"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2830383\" target=\"_blank\" rel=\"noopener noreferrer\">Quality improvement study published in JAMA Network Open<\/a> He followed 46 professionals from 17 specialties in a U.S. academic system. After incorporating an environmental writer, he observed associations with less time per note, more closings on the same day, less off-hours work, and a perceived lower mental workload. The design was pre-post, without a control group, and the setting was not Latin American; its results show potential, not a transferable guarantee to any clinic.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The practical conclusion is not that every automatically generated note is better. It is that preparing a first draft can take a task of composing from scratch. The benefit depends on three conditions: that the structure corresponds to the type of query, that reviewing is faster than rewriting, and that the professional retains the final approval.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This principle is evident in the <a href=\"\/en\/clinical-notes-ai\/\">AI-assisted clinical notes<\/a>The result should remain a revisable draft. The professional edits and decides what is part of the record; the automatic generation does not convert the content into final documentation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">After the consultation: use the context once, don't reconstruct it several times<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At the end of the meeting, tasks often appear that repeat information already discussed. The concentration observed in recipes and orders places the use in routine and actionable outputs: to re-use the context that the professional has already verified, rather than re-writing it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This continuity reduces two frictions. The first is to rewrite data that is already in the note. The second is to lose the relationship between the documented information and what is delivered to the patient. A coherent flow keeps both elements close together and makes it clear what needs revision before sharing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same principle applies to the following query. A useful longitudinal record is not a larger file, but a way to revisit treatments, results, decisions, and pending tasks without having to reread the entire history. For the professional, the important question is whether the system returns the correct context when needed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Documentation is the first pillar, not the only one<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Starting with documentation makes sense because the problem is common, visible, and easy to prove. It is also a use where professional supervision fits naturally: a draft is prepared and reviewed before it is incorporated into the file.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, a clinical platform is incomplete if it only writes notes. Doctors also need to resolve specific questions, review a recommendation, compare sources, and prepare documents related to the encounter. These tasks share context; treating them as separate applications forces the professional to repeat them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A coherent clinical architecture connects documentation with <a href=\"\/en\/answers\/\">clinical answers with sources<\/a>, a deeper investigation when the question requires it and <a href=\"\/en\/medical-documents-with-ai\/\">medical documents prepared from the revised context<\/a>. The practical distinction is important: not every doubt requires an extensive review and none of these layers should delegate the clinical decision-making.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Visible sources: a practical difference compared to generic AI<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A clinical response is more useful when it allows one to open up the references and understand where an assertion comes from. This does not eliminate the need to assess quality, current status, and applicability, but it changes the interaction: the professional receives not just a conclusion; they receive a starting point that they can verify before deciding how to use the information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/www.who.int\/news\/item\/18-01-2024-who-releases-ai-ethics-and-governance-guidance-for-large-multi-modal-models\" target=\"_blank\" rel=\"noopener noreferrer\">WHO guide on multimodal models in health<\/a> It recommends designing systems for well-defined tasks, involving professionals and other stakeholders, and maintaining evaluation and monitoring throughout the life cycle. <a href=\"https:\/\/www.paho.org\/es\/inteligencia-artificial-para-salud\" target=\"_blank\" rel=\"noopener noreferrer\">OPS<\/a> It places AI within a broader transformation that includes governance, interoperability, capabilities, and data protection.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That framework helps avoid superficial comparisons between tools. It\u2019s not enough to ask what model a platform uses. It\u2019s more important to ask what task it solves, what information it receives, how it displays the sources, who reviews the output, and how the result is integrated with existing work.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What changes when adoption takes place in a clinic<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For an individual doctor, a tool can be useful from the first consultation. For a clinic, the result depends on shared processes as well. Buying access does not equate to changing the operation.<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table><thead><tr><th>Adoption question<\/th><th>What is useful to define<\/th><\/tr><\/thead><tbody><tr><td><strong>Where the flow begins<\/strong><\/td><td>What information arrives before the consultation and who verifies that it is available.<\/td><\/tr><tr><td><strong>What is being reviewed<\/strong><\/td><td>What results does AI prepare, which professionals approve them and at what time.<\/td><\/tr><tr><td><strong>What is shared<\/strong><\/td><td>How to keep notes, documents, and appointments in order among professionals without duplicating work.<\/td><\/tr><tr><td><strong>How it is evaluated<\/strong><\/td><td>Real adoption, review time, tasks completed and issues detected; not just created accounts.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/www.paho.org\/es\/sistemas-informacion-salud-digital\/8-principios-para-transformacion-digital-sector-salud\" target=\"_blank\" rel=\"noopener noreferrer\">OPS points out that digital transformation involves cultural changes<\/a>, In addition to technology. In 2026, the OPS and the IDB also published <a href=\"https:\/\/www.paho.org\/en\/documents\/ai-guard-tool-governance-use-assessment-responsible-deployment\" target=\"_blank\" rel=\"noopener noreferrer\">AI-GUARD<\/a>, a framework for evaluating strategic value, risks, governance, human oversight, and bias mitigation before implementing AI in healthcare. In practice, a clinic needs to explain what improves, what doesn\u2019t change, who is responsible for each outcome, and what control the professional retains.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For teams, the adoption unit should not be a collection of licenses, but a <a href=\"\/en\/software-for-clinics\/\">shared clinical flow<\/a>. The implementation must define how the context supports the care, what each professional reviews, and how the work ends without duplicating it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What we can affirm today and what remains to be measured<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The available literature allows us to conclude that environmental documentation has the potential to reduce some of the burden of note-taking, and that generative AI can support administrative and informational tasks. It also makes clear that quality, safety, and usefulness depend on the design of the task, the context, and supervision.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It does not allow us to conclude that all professionals will save the same amount of time, that all notes will be more complete, or that the results of a health system will be transferred unchanged to Latin America. It is also not enough to measure how many times a function is opened: to understand value, stable definitions of user, session, task completed, and review are needed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The internal data of this report allow to describe observed use, not clinical efficacy or time savings. A completed action indicates that the flow reached a valid final state within Itaca; it does not in itself demonstrate that the result was incorporated into the record, improved a decision, or reduced the workload of the professional. Those questions require additional measurements.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"metodologia\">How we conduct this analysis<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>Period of internal data:<\/strong> From January 1 to September 24, 2026, UTC. September 24 is the last full day included.<\/li><li><strong>Observed population:<\/strong> External users with at least one valid action in one of five measured flows. The size of the cohort is not published. \u201cActive user\u201d does not mean registered user or subscriber.<\/li><li><strong>Action steps:<\/strong> Regular clinical responses deep research, documentation sessions with completed notes, completed documents, and shared questionnaires.<\/li><li><strong>Conversations:<\/strong> a response was classified as follow-up when it was linked to a previous question from the same user. The latency was calculated between the completion of a response and the completion of its follow-up response; negative intervals were excluded.<\/li><li><strong>Documentation turnaround time:<\/strong> Active days and weeks were calculated among users with at least one completed session. They are not interpreted as retention because each user had a different observation window.<\/li><li><strong>Exclusions:<\/strong> internal users, demonstration records, discarded, excluded or failed events, and events that did not reach the final state defined for each flow.<\/li><li><strong>Questionnaires:<\/strong> The seven-and-thirty-day rates use only questionnaires shared with a full thirty-day window. The median time was calculated only between the questionnaires completed within that timeframe.<\/li><li><strong>Country and specialty:<\/strong> These are fields declared in the profile. The country was available to 99.4 % % of active users. Mexico, Peru, Colombia, and Ecuador accounted for 64.4% % of those who declared a country; this distribution describes the cohort of , Itaca not the Latin American market. The specialty was available only to 28.5%, % so it was not used to compare behaviors across specialties.<\/li><li><strong>Privacy:<\/strong> The analysis was conducted using aggregations. No clinical content, patient identity, or user identifiers were consulted or published.<\/li><li><strong>External sources:<\/strong> WHO and OPS guidance on adoption, governance, and digital transformation, in addition to peer-reviewed scientific literature.<\/li><li><strong>Scope:<\/strong> observational analysis of use within Itaca. It is not a regional survey, a prevalence estimate, or an evaluation of clinical results.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Limitations<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Evidence on generative AI in clinical practice changes rapidly, and a significant portion comes from healthcare systems outside of Latin America. The data are biased: Itaca they reflect those who used the platform and completed a flow, not all registered users or all healthcare professionals. Later enrolled users had less time to repeat activity; therefore, the active months figures were not interpreted as retention. Nor were full months compared to September, which only includes data up to the 24th. Flows, regulations, infrastructure, and documentation practices vary between countries and organizations. This report identifies patterns and useful questions; it does not replace the technical, clinical, operational, and regulatory assessment required for each implementation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Sources consulted<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.ama-assn.org\/sites\/ama-assn.org\/files\/2026-03\/physician-ai-sentiment-report_0.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">American Medical Association: 2026 Physician Survey on Augmented Intelligence<\/a>.<\/li><li><a href=\"https:\/\/www.paho.org\/en\/news\/18-11-2025-artificial-intelligence-and-health-workforce-paho-presents-results-regional-study\" target=\"_blank\" rel=\"noopener noreferrer\">OPS: Regional study on artificial intelligence and the health workforce<\/a>.<\/li><li><a href=\"https:\/\/www.paho.org\/en\/documents\/ai-guard-tool-governance-use-assessment-responsible-deployment\" target=\"_blank\" rel=\"noopener noreferrer\">OPS and BID: AI-GUARD, an assessment for the responsible deployment of AI<\/a>.<\/li><li><a href=\"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2830383\" target=\"_blank\" rel=\"noopener noreferrer\">JAMA Network Open: Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency<\/a>.<\/li><li><a href=\"https:\/\/www.who.int\/news\/item\/18-01-2024-who-releases-ai-ethics-and-governance-guidance-for-large-multi-modal-models\" target=\"_blank\" rel=\"noopener noreferrer\">WHO: guidance on ethics and governance of multimodal health models<\/a>.<\/li><li><a href=\"https:\/\/www.paho.org\/es\/inteligencia-artificial-para-salud\" target=\"_blank\" rel=\"noopener noreferrer\">OPS: Artificial Intelligence for Health<\/a>.<\/li><li><a href=\"https:\/\/www.paho.org\/es\/sistemas-informacion-salud-digital\/8-principios-para-transformacion-digital-sector-salud\" target=\"_blank\" rel=\"noopener noreferrer\">OPS: Eight principles for the digital transformation of the healthcare sector<\/a>.<\/li><\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\">This report is part of the <a href=\"\/en\/reports\/\">Reports of Itaca over-reliance on AI in medical practice<\/a>.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-group alignfull has-background-alt-background-color has-background is-layout-constrained wp-container-core-group-is-layout-e0b943a3 wp-block-group-is-layout-constrained\" style=\"padding-top:4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:4rem;padding-left:var(--wp--preset--spacing--40)\">\n<h2 class=\"wp-block-heading\">Know the complete clinical flow of Itaca<\/h2>\n\n\n<p class=\"wp-block-paragraph\">See how it Itaca helps you prepare notes and documents, answer questions with sources, and retain context between queries.<\/p>\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-foregound-alt-color has-heading-color-background-color has-text-color has-background wp-element-button\" href=\"\/en\/ai-for-physicians\/\" style=\"border-radius:100px;text-decoration:none\">Knowing Itaca for doctors<\/a><\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>An\u00e1lisis sobre c\u00f3mo la IA pasa de la experimentaci\u00f3n a flujos cl\u00ednicos concretos, con evidencia p\u00fablica, marcos regionales y una se\u00f1al agregada de uso en Latinoam\u00e9rica.<\/p>","protected":false},"author":1,"featured_media":0,"parent":5083,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"nf_dc_page":"","slim_seo":[],"_joinchat":[],"footnotes":""},"class_list":["post-5084","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/pages\/5084","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/comments?post=5084"}],"version-history":[{"count":20,"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/pages\/5084\/revisions"}],"predecessor-version":[{"id":5145,"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/pages\/5084\/revisions\/5145"}],"up":[{"embeddable":true,"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/pages\/5083"}],"wp:attachment":[{"href":"https:\/\/itaca.ai\/en\/wp-json\/wp\/v2\/media?parent=5084"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}