{"id":9923,"date":"2025-06-29T17:07:12","date_gmt":"2025-06-29T14:07:12","guid":{"rendered":"https:\/\/medilab.km.ua\/?p=9923"},"modified":"2026-10-03T11:47:26","modified_gmt":"2026-10-03T11:47:26","slug":"artificial-intelligence-in-laboratories-transformation-of-medical-diagnostics","status":"publish","type":"post","link":"https:\/\/medilab.km.ua\/en\/shtuchnyj-intelekt-u-laboratoriyah-transformatsiya-medychnoyi-diagnostyky\/","title":{"rendered":"Artificial Intelligence in Laboratories: Transforming Medical Diagnostics"},"content":{"rendered":"<h2 data-start=\"417\" data-end=\"463\">A new stage in the development of laboratory medicine<\/h2>\n<p data-start=\"465\" data-end=\"724\">The medical laboratory is no longer just a place for collecting and analyzing biomaterial. With the introduction <strong data-start=\"563\" data-end=\"601\">artificial intelligence (AI) technologies<\/strong>, she becomes <strong data-start=\"613\" data-end=\"647\">dynamic think tank<\/strong>, where the speed, accuracy and adaptability of processes change in real time.<\/p>\n<p data-start=\"726\" data-end=\"935\"><strong data-start=\"726\" data-end=\"759\">AI in laboratory diagnostics<\/strong> \u2014 it is not a replacement for personnel, but a tool that <strong data-start=\"805\" data-end=\"835\">expands human capabilities<\/strong>: in classification, forecasting, logistics optimization, results interpretation and quality control.<\/p>\n<p data-start=\"726\" data-end=\"935\"><img decoding=\"async\" class=\"alignnone size-full wp-image-9925\" src=\"https:\/\/medilab.km.ua\/wp-content\/themes\/woodmart\/images\/lazy.svg\" data-src=\"https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training.jpeg\" alt=\"artificial intelligence-based medical system interface\" width=\"1200\" height=\"800\" srcset=\"\" data-srcset=\"https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training.jpeg 1200w, https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training-150x100.jpeg 150w, https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training-600x400.jpeg 600w, https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training-300x200.jpeg 300w, https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training-1024x683.jpeg 1024w, https:\/\/medilab.km.ua\/wp-content\/uploads\/2025\/06\/Kate_Anne_Training-768x512.jpeg 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<h2 data-start=\"937\" data-end=\"990\">How exactly is AI used in medical laboratories?<\/h2>\n<h3 data-start=\"992\" data-end=\"1037\">1. Automated image recognition<\/h3>\n<p data-start=\"1039\" data-end=\"1185\">Computer vision systems trained on millions of samples are capable of <strong data-start=\"1110\" data-end=\"1173\">recognize blood cells, parasites, pathogenic morphologies<\/strong>. For example:<\/p>\n<ul data-start=\"1187\" data-end=\"1379\">\n<li data-start=\"1187\" data-end=\"1250\">\n<p data-start=\"1189\" data-end=\"1250\">Recognition of atypical cells in hematology (leukemia, lymphoma)<\/p>\n<\/li>\n<li data-start=\"1251\" data-end=\"1323\">\n<p data-start=\"1253\" data-end=\"1323\">Detection of parasites (malaria) or mycobacteria (tuberculosis) on smears<\/p>\n<\/li>\n<li data-start=\"1324\" data-end=\"1379\">\n<p data-start=\"1326\" data-end=\"1379\">Automatic classification of sperm in a spermogram<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"1381\" data-end=\"1451\">It <strong data-start=\"1384\" data-end=\"1438\">reduces the human factor and increases reproducibility<\/strong> results.<\/p>\n<h3 data-start=\"1453\" data-end=\"1503\">2. Interpretation of complex biochemical profiles<\/h3>\n<p data-start=\"1505\" data-end=\"1668\">Machine learning algorithms analyze <strong data-start=\"1545\" data-end=\"1565\">more than one indicator<\/strong>, and <strong data-start=\"1569\" data-end=\"1595\">complete biochemical panels<\/strong>, revealing patterns that are not obvious even to an experienced laboratory technician:<\/p>\n<ul data-start=\"1670\" data-end=\"1927\">\n<li data-start=\"1670\" data-end=\"1768\">\n<p data-start=\"1672\" data-end=\"1768\">Dynamics of enzyme levels (ALT, AST, GGT) + bilirubin \u2192 early detection of liver failure<\/p>\n<\/li>\n<li data-start=\"1769\" data-end=\"1846\">\n<p data-start=\"1771\" data-end=\"1846\">Correlation of glucose, insulin and C-peptide \u2192 clarification of the type of diabetes mellitus<\/p>\n<\/li>\n<li data-start=\"1847\" data-end=\"1927\">\n<p data-start=\"1849\" data-end=\"1927\">Interpretation of hormonal profile taking into account age, cycle, and concomitant conditions<\/p>\n<\/li>\n<\/ul>\n<h3 data-start=\"1929\" data-end=\"1986\">3. Risk prediction based on laboratory data<\/h3>\n<p data-start=\"1988\" data-end=\"2119\">AI-based systems can form <strong data-start=\"2024\" data-end=\"2058\">individual risk profiles<\/strong> patients, even if the values are still within normal limits:<\/p>\n<ul data-start=\"2121\" data-end=\"2352\">\n<li data-start=\"2121\" data-end=\"2208\">\n<p data-start=\"2123\" data-end=\"2208\">Prediction of cardiovascular events by lipid profile, homocysteine, C-reactive protein<\/p>\n<\/li>\n<li data-start=\"2209\" data-end=\"2272\">\n<p data-start=\"2211\" data-end=\"2272\">Assessment of the risk of preeclampsia in pregnant women using early biomarkers<\/p>\n<\/li>\n<li data-start=\"2273\" data-end=\"2352\">\n<p data-start=\"2275\" data-end=\"2352\">Algorithms for early detection of oncological processes based on tumor markers in dynamics<\/p>\n<\/li>\n<\/ul>\n<h2 data-start=\"2354\" data-end=\"2405\">Advantages of using AI in laboratory practice<\/h2>\n<div class=\"_tableContainer_80l1q_1\">\n<div class=\"_tableWrapper_80l1q_14 group flex w-fit flex-col-reverse\">\n<div class=\"table-scroll\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"2407\" data-end=\"2924\">\n<thead data-start=\"2407\" data-end=\"2480\">\n<tr data-start=\"2407\" data-end=\"2480\">\n<th data-start=\"2407\" data-end=\"2437\" data-col-size=\"sm\">Advantage<\/th>\n<th data-start=\"2437\" data-end=\"2480\" data-col-size=\"sm\">Practical effect<\/th>\n<\/tr>\n<\/thead>\n<tbody data-start=\"2555\" data-end=\"2924\">\n<tr data-start=\"2555\" data-end=\"2628\">\n<td data-start=\"2555\" data-end=\"2585\" data-col-size=\"sm\">Higher accuracy<\/td>\n<td data-start=\"2585\" data-end=\"2628\" data-col-size=\"sm\">Minimizing interpretation errors<\/td>\n<\/tr>\n<tr data-start=\"2629\" data-end=\"2702\">\n<td data-start=\"2629\" data-end=\"2659\" data-col-size=\"sm\">Processing speed<\/td>\n<td data-start=\"2659\" data-end=\"2702\" data-col-size=\"sm\">Reducing the time to result delivery<\/td>\n<\/tr>\n<tr data-start=\"2703\" data-end=\"2776\">\n<td data-start=\"2703\" data-end=\"2733\" data-col-size=\"sm\">Scalability<\/td>\n<td data-start=\"2733\" data-end=\"2776\" data-col-size=\"sm\">Analyzing tens of thousands of tests daily<\/td>\n<\/tr>\n<tr data-start=\"2777\" data-end=\"2850\">\n<td data-start=\"2777\" data-end=\"2807\" data-col-size=\"sm\">Standardization<\/td>\n<td data-start=\"2807\" data-end=\"2850\" data-col-size=\"sm\">Reducing interlaboratory variations<\/td>\n<\/tr>\n<tr data-start=\"2851\" data-end=\"2924\">\n<td data-start=\"2851\" data-end=\"2881\" data-col-size=\"sm\">Clinical decision support<\/td>\n<td data-start=\"2881\" data-end=\"2924\" data-col-size=\"sm\">Helping the doctor in choosing tactics<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n<div class=\"sticky end-(--thread-content-margin) h-0 self-end select-none\">\n<div class=\"absolute end-0 flex items-end\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 data-start=\"2926\" data-end=\"2949\">Challenges and limitations<\/h2>\n<p data-start=\"2951\" data-end=\"3023\">Despite the rapid development, the implementation of AI in <a href=\"https:\/\/medilab.km.ua\/en\/what-is-the-same-laboratory-medi-lab\/\" title=\"WHY MEDI LAB+ LABORATORY\"  data-wpil-monitor-id=\"429\">laboratories<\/a> has its own barriers:<\/p>\n<ul data-start=\"3025\" data-end=\"3464\">\n<li data-start=\"3025\" data-end=\"3103\">\n<p data-start=\"3027\" data-end=\"3103\"><strong data-start=\"3027\" data-end=\"3056\">Legal responsibility<\/strong>: who is responsible for the error \u2014 the system or the doctor?<\/p>\n<\/li>\n<li data-start=\"3104\" data-end=\"3221\">\n<p data-start=\"3106\" data-end=\"3221\"><strong data-start=\"3106\" data-end=\"3131\">Transparency of algorithms<\/strong>: in complex neural networks, it is difficult to track why a particular conclusion was made.<\/p>\n<\/li>\n<li data-start=\"3222\" data-end=\"3353\">\n<p data-start=\"3224\" data-end=\"3353\"><strong data-start=\"3224\" data-end=\"3258\">The need for validated data<\/strong>: algorithms should work on standardized sets adapted to a specific population.<\/p>\n<\/li>\n<li data-start=\"3354\" data-end=\"3464\">\n<p data-start=\"3356\" data-end=\"3464\"><strong data-start=\"3356\" data-end=\"3374\">Ethical aspect<\/strong>: personal data processed by the systems must be protected at the HIPAA \/ GDPR level.<\/p>\n<\/li>\n<\/ul>\n<h2 data-start=\"3466\" data-end=\"3488\">Practical examples<\/h2>\n<ul data-start=\"3490\" data-end=\"3951\">\n<li data-start=\"3490\" data-end=\"3629\">\n<p data-start=\"3492\" data-end=\"3629\">\u0423 <strong data-start=\"3494\" data-end=\"3511\">USA and Japan<\/strong> AI modules are used to sort Papanicolaou smears, which has reduced the workload on cytologists at 40%.<\/p>\n<\/li>\n<li data-start=\"3630\" data-end=\"3790\">\n<p data-start=\"3632\" data-end=\"3790\">\u0423 <strong data-start=\"3634\" data-end=\"3647\">Germany<\/strong> Artificial intelligence helps in the early detection of sepsis based on changes in the hematological profile 12\u201324 hours before the appearance of clinical symptoms.<\/p>\n<\/li>\n<li data-start=\"3791\" data-end=\"3951\">\n<p data-start=\"3793\" data-end=\"3951\">In Ukraine, they are actively being implemented <strong data-start=\"3826\" data-end=\"3853\">laboratory LIS systems<\/strong>, which integrate algorithms for preliminary analysis of results, with a warning about critical values.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"3958\" data-end=\"4249\"><a href=\"https:\/\/medilab.km.ua\/en\/artificial-intelligence-in-medicine-as-a-technology-that-changes-the-health-care-system\/\">Artificial intelligence in laboratory medicine<\/a> \u2014 not the future, but <strong data-start=\"4016\" data-end=\"4041\">already existing reality<\/strong>, which transforms the role of the laboratory technician from an \u00aboperator\u00bb to <strong data-start=\"4097\" data-end=\"4121\">clinical analyst<\/strong>, which makes decisions together with a digital system. This opens up a new level of precision, personalization and efficiency in medicine. Thanks to the integration of artificial intelligence and innovative technologies such as <a href=\"https:\/\/medilab.km.ua\/en\/3d-printers-in-laboratories-new-possibilities-for-medicine-biotechnology-and-scientific-research\/\">possibilities of 3D printers in medicine<\/a>, doctors are given new tools to create personalized medical solutions. These technologies not only raise the standards of treatment, but also provide patients with more precise and effective prostheses and implants adapted to their specific needs. Such changes contribute not only to improved clinical outcomes, but also to an overall improvement in the quality of life of patients.<\/p>","protected":false},"excerpt":{"rendered":"<p>A new stage in the development of laboratory medicine The medical laboratory is no longer just a place for collecting and analyzing biomaterial. With the introduction<\/p>","protected":false},"author":627,"featured_media":9924,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_flow_reviewer_id":0,"_flow_review_status":""},"categories":[238],"tags":[],"class_list":["post-9923","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-novyny-medi-lab-plyus"],"_links":{"self":[{"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/posts\/9923","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/users\/627"}],"replies":[{"embeddable":true,"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/comments?post=9923"}],"version-history":[{"count":0,"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/posts\/9923\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/media\/9924"}],"wp:attachment":[{"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/media?parent=9923"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/categories?post=9923"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/medilab.km.ua\/en\/wp-json\/wp\/v2\/tags?post=9923"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}