{"id":5115,"date":"2025-08-14T10:18:42","date_gmt":"2025-08-14T10:18:42","guid":{"rendered":"https:\/\/maxymia.com\/?p=5115"},"modified":"2025-10-07T10:15:31","modified_gmt":"2025-10-07T10:15:31","slug":"ia-en-medicina-casos-reales-de-aplicacion","status":"publish","type":"post","link":"https:\/\/maxymia.com\/en\/ia-en-medicina-casos-reales-de-aplicacion\/","title":{"rendered":"AI in medicine: Real-life application cases"},"content":{"rendered":"<p>AI does not \u201creplace\u201d healthcare personnel: <strong>multiplies its reach<\/strong>. It automates repetitive tasks, finds patterns in data that are impossible to review manually, and suggests decisions that the clinical team then validates. The interesting thing: we&#039;re no longer talking about promises, <strong>but of results in hospitals and national programs<\/strong>.<\/p>\n\n\n\n<div class=\"wp-block-rank-math-toc-block\" id=\"rank-math-toc\"><p>Table of contents<\/p><nav><ul><li><a href=\"#ejemplos-y-casos-reales-de-ia-en-medicina\">Real-life examples and cases of AI in medicine<\/a><ul><li><a href=\"#1-deteccion-precoz-de-cancer-de-mama\">1) Early detection of breast cancer<\/a><\/li><li><a href=\"#2-retinopatia-diabetica-en-programas-nacionales\">2) Diabetic retinopathy in national programs<\/a><\/li><li><a href=\"#3-dermatologia-melanoma-y-cancer-cutaneo\">3) Dermatology: melanoma and skin cancer<\/a><\/li><li><a href=\"#4-cardiologia-fibrilacion-auricular-con-wearables\">4) Cardiology: atrial fibrillation with wearables<\/a><\/li><li><a href=\"#5-sepsis-alertas-tempranas-que-cambian-resultados\">5) Sepsis: early warnings that change outcomes<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ejemplos-y-casos-reales-de-ia-en-medicina\">Real-life examples and cases of AI in medicine<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-deteccion-precoz-de-cancer-de-mama\">1) Early detection of breast cancer<\/h3>\n\n\n\n<p>The <strong>artificial intelligence (AI)<\/strong> applied to the <strong>mammography<\/strong> is improving <strong>early detection of breast cancer<\/strong>, reducing workload and maintaining or even improving diagnostic accuracy. A <a href=\"https:\/\/www.thelancet.com\/journals\/lanonc\/article\/PIIS1470-2045%2823%2900298-X\/abstract\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">randomized trial in <strong>Sweden<\/strong><\/a> demonstrated that AI, as a supporting reader, is not only clinically safe, but also reduces radiologists&#039; workload by half.<\/p>\n\n\n\n<p>The <a href=\"https:\/\/www.nature.com\/articles\/s41591-024-03408-6\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">recent implementations<\/a>, have increased the detection of cancers without increasing the <strong>false positives<\/strong>. In addition, advanced models now make it possible to more efficiently select patients who need a <strong>magnetic resonance imaging<\/strong> complementary screening after a negative mammogram. The clinical impact is clear: earlier detection, fewer unnecessary second readings, and a <strong>triage<\/strong> more efficient.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-retinopatia-diabetica-en-programas-nacionales\">2) Diabetic retinopathy in national programs<\/h3>\n\n\n\n<p>The <strong>artificial intelligence (AI)<\/strong> is being used on a large scale in the <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2162098924000975\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><strong>screening for diabetic retinopathy<\/strong><\/a>, improving the detection and management of this eye disease. In <strong>Thailand<\/strong>, AI has been implemented in multiple centers with <strong>real-time reading<\/strong>, demonstrating its viability and positive impact on the public health system, according to studies in <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/35272972\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><strong>PubMed<\/strong><\/a>.<\/p>\n\n\n\n<p>Recent integrations combine <strong>vision and language<\/strong>, optimizing workflows in primary care, facilitating faster screenings, <strong>more precise derivations<\/strong> and a minor <strong>retinal saturation<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-dermatologia-melanoma-y-cancer-cutaneo\">3) Dermatology: melanoma and skin cancer<\/h3>\n\n\n\n<p>Artificial intelligence (AI) is revolutionizing the diagnosis of <strong>melanoma<\/strong> and <strong>skin cancer<\/strong>, providing support even with hard-to-detect injuries. A <a href=\"https:\/\/www.nature.com\/articles\/s43856-024-00598-5\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">multicenter study conducted in 8 hospitals<\/a> showed that AI algorithms outperformed dermatologists in the <strong>detection of melanoma in complex cases<\/strong>, which highlights its potential to improve diagnostic accuracy.<\/p>\n\n\n\n<p>Regulatory advances and the use of techniques such as <a href=\"https:\/\/jamanetwork.com\/journals\/jamadermatology\/fullarticle\/2814691\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><strong>federated learning<\/strong><\/a> They are facilitating the integration of AI-assisted devices into primary care, improving diagnostic privacy and efficiency, and optimizing the triage process.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"4-cardiologia-fibrilacion-auricular-con-wearables\">4) Cardiology: atrial fibrillation with wearables<\/h3>\n\n\n\n<p>The <strong>smartwatches<\/strong> and <strong>Portable ECGs<\/strong> With artificial intelligence they are improving early detection of <strong>atrial fibrillation (AF)<\/strong>, allowing passive alerts that facilitate continuous monitoring. Recent studies, such as those of <a href=\"https:\/\/academic.oup.com\/ehjdh\/article\/5\/5\/535\/7708688\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><strong>Oxford Academic<\/strong><\/a> and <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666389924000783\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><strong>ScienceDirect<\/strong><\/a>, have demonstrated improvements in the detection of AF episodes using predictive models and clock ECG.<\/p>\n\n\n\n<p>Although the benefits are clear, diagnostic confirmation and reduction of <strong>false positives<\/strong>. This is leading to a clinical approach to <strong>remote monitoring<\/strong> and <strong>event prevention<\/strong>, allowing for faster referrals and more proactive disease management.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"5-sepsis-alertas-tempranas-que-cambian-resultados\">5) Sepsis: early warnings that change outcomes<\/h3>\n\n\n\n<p>The <strong>sepsis<\/strong> requires rapid intervention to improve outcomes, and AI systems such as <strong>COMPOSER<\/strong> have been shown to reduce mortality and improve compliance with treatment protocols in hospitals. A <a href=\"https:\/\/www.nature.com\/articles\/s41746-023-00986-6\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">study<\/a> highlights how these systems help manage the \u201cgolden hour\u201d more effectively.<\/p>\n\n\n\n<p><a href=\"https:\/\/eurjmedres.biomedcentral.com\/articles\/10.1186\/s40001-024-01756-0\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Recent research<\/a> validate predictive models to anticipate the onset and mortality of sepsis. The key is to integrate these models into the clinical flow, as in the <strong>electronic health records (EHR)<\/strong>, with clear and actionable alerts that optimize its actual use.<\/p>\n\n\n\n<p>These cases make it clear that AI in medicine <strong>is already present <\/strong>and is a clear ally for healthcare professionals and scientists.<\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>AI doesn&#039;t &quot;replace&quot; healthcare personnel: it multiplies their reach. It automates repetitive tasks, finds patterns in data that are impossible to review manually, and suggests decisions that the clinical team then validates. The interesting thing: we&#039;re no longer talking about promises, but about results in hospitals and national programs. Real-life examples and cases of AI in medicine 1) [\u2026]<\/p>","protected":false},"author":109,"featured_media":5116,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kadence_starter_templates_imported_post":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[20],"tags":[],"class_list":["post-5115","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-medicina"],"blocksy_meta":[],"acf":[],"jetpack_featured_media_url":"https:\/\/maxymia.com\/wp-content\/uploads\/2025\/08\/ia-en-medicina.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/posts\/5115","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/users\/109"}],"replies":[{"embeddable":true,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/comments?post=5115"}],"version-history":[{"count":2,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/posts\/5115\/revisions"}],"predecessor-version":[{"id":5281,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/posts\/5115\/revisions\/5281"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/media\/5116"}],"wp:attachment":[{"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/media?parent=5115"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/categories?post=5115"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/maxymia.com\/en\/wp-json\/wp\/v2\/tags?post=5115"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}