rfhealthЗдравоохранение Российской ФедерацииHealth care of the Russian Federation0044-197X2412-0723Federal Scientific Center of Hygiene named after F.F. Erisman10.47470/0044-197X-2025-69-2-117-122uaoiterfhealth-1849Research ArticleОРГАНИЗАЦИЯ ЗДРАВООХРАНЕНИЯHEALTH CARE ORGANIZATIONПерспективы внедрения технологий искусственного интеллекта и компьютерного зрения в лабораторной медицине (обзор литературы)Prospects for the implementation of artificial intelligence and computer vision technologies in laboratory medicine (literature review)https://orcid.org/0000-0002-3650-6121ТрегубПавел ПавловичTregubPavel P.

Доктор мед. наук, руководитель производственного комплекса по лабораторной диагностике ФБУН ЦНИИ эпидемиологии Роспотребнадзора, 111123, Москва, Россия

e-mail: tregub@cmd.su

DSc (Medicine), Head of the Laboratory Diagnostics Production Complex, Central Research Institute of Epidemiology, Moscow, 111123, Russian Federation

e-mail: tregub@cmd.su

tregub@cmd.suhttps://orcid.org/0000-0002-7461-5329ЖемчугинДмитрий ЕвгеньевичZhemchuginDmitry E.

Врач-трансфузиолог, ГБУЗ «ГКБ им. М.П. Кончаловского ДЗМ», 124489, Зеленоград, Россия

e-mail: Dmitriy_Zh@mail.ru

Transfusiologist, Municipal Clinical Hospital named after M.P. Konchalovsky of the Moscow City Health Department, Zelenograd, 124489, Russian Federation

e-mail: Dmitriy_Zh@mail.ru

Dmitriy_Zh@mail.ruhttps://orcid.org/0009-0007-7495-8907ЗубановПавел СергеевичZubanovPavel S.

Зам. руководителя производственного комплекса по лабораторной диагностике ФБУН ЦНИИ эпидемиологии Роспотребнадзора, 111123, Москва, Россия

e-mail: zubanov@cmd.su

Deputy Head of the Production Complex for Laboratory Diagnostics, Central Research Institute of Epidemiology, Moscow, 111123, Russian Federation

e-mail: zubanov@cmd.su

zubanov@cmd.suhttps://orcid.org/0000-0002-2787-4731ГольдбергАркадий СтаниславовичGoldbergArkady S.

Канд. мед. наук, проректор по экономике и развитию, ФГБОУ ДПО РМАНПО Минздрава России, 125993, Москва, Россия

e-mail: goldarcadiy@gmail.com

PhD, (Medicine), Vice Rector for Economics and Development of the Russian Medical Academy of Continuous Professional Education, Moscow, 125993, Russian Federation

e-mail: goldarcadiy@gmail.com

goldarcadiy@gmail.comhttps://orcid.org/0000-0002-0854-8076ГодковМихаил АндреевичGodkovMikhail A.

Доктор мед. наук, профессор, зав. кафедрой КЛД с курсом лабораторной иммунологии ФГБОУ ДПО РМАНПО Минздрава России, 125993, Москва, Россия

e-mail: mgodkov@yandex.ru

DSc (Medicine), Professor, Head of the Department of Clinical Diagnostics with a Course in Laboratory Immunology, Russian Medical Academy of Continuous Professional Education, Moscow, 125993, Russian Federation

e-mail: mgodkov@yandex.ru

mgodkov@yandex.ruhttps://orcid.org/0000-0003-4228-9044АкимкинВасилий ГеннадьевичAkimkinVasily G.

Академик РАН, доктор мед. наук, профессор, директор ФБУН ЦНИИ эпидемиологии Роспотребнадзора, 111123, Москва, Россия

e-mail: vgakimkin@yandex.ru

DSc (Medicine), Academician of the Russian Academy of Sciences, Professor, Director of the Central Research Institute of Epidemiology, Moscow, 111123, Russian Federation

e-mail: vgakimkin@yandex.ru

vgakimkin@yandex.ruФБУН «Центральный научно-исследовательский институт эпидемиологии» Федеральной службы по надзору в сфере защиты прав потребителей и благополучия человека; ФГАОУ ВО Первый Московский государственный медицинский университет имени И.М. Сеченова Министерства здравоохранения Российской Федерации (Сеченовский Университет); ФГБНУ «Научный центр неврологии» Министерства науки и высшего образования Российской ФедерацииCentral Research Institute of Epidemiology; First Moscow State Medical University named after I.M. Sechenov (Sechenov University; Scientific Center of NeurologyГБУЗ города Москвы «Городская клиническая больница имени М.П. Кончаловского Департамента здравоохранения города Москвы»; ГБУЗ МО «Московский областной научно-исследовательский клинический институт имени М.Ф. Владимирского»Municipal Clinical Hospital named after M.P. Konchalovsky; Moscow Regional Research Clinical Institute named after M.F. VladimirskyФБУН «Центральный научно-исследовательский институт эпидемиологии» Федеральной службы по надзору в сфере защиты прав потребителей и благополучия человекаCentral Research Institute of EpidemiologyФГБОУ ДПО «Российская медицинская академия непрерывного профессионального образования» Министерства здравоохранения Российской ФедерацииRussian Medical Academy of Continuous Professional EducationФГБОУ ДПО «Российская медицинская академия непрерывного профессионального образования» Министерства здравоохранения Российской Федерации; ГБУЗ «Научно-исследовательский институт скорой помощи имени Н.В. Склифосовского Департамента здравоохранения города Москвы»Russian Medical Academy of Continuous Professional Education; N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow City Health Department202530042025692117122Copyright © Трегуб П.П., Жемчугин Д.Е., Зубанов П.С., Гольдберг А.С., Годков М.А., Акимкин В.Г., 20252025Трегуб П.П., Жемчугин Д.Е., Зубанов П.С., Гольдберг А.С., Годков М.А., Акимкин В.Г.Tregub P.P., Zhemchugin D.E., Zubanov P.S., Goldberg A.S., Godkov M.A., Akimkin V.G.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rfhealth.ru/jour/article/view/1849

Лабораторная диагностика играет одну из ведущих ролей в современной медицине, предоставляя врачам клинических специальностей данные для своевременной установки диагноза, выбора тактики и методов лечения. Для обеспечения высокой эффективности и повышения точности исследований в последнее время в практику работы лабораторной службы активно внедряются технологии искусственного интеллекта (ИИ): компьютерное зрение (КЗ), машинное обучение, глубокое обучение, нейронные сети, анализ банка данных. В лабораторной диагностике эти технологии успешно используются для автоматизации и улучшения технологических процессов, включая обработку результатов реакций, цитоморфологических изображений, анализ полученных данных. Одним из перспективных направлений внедрения ИИ в лабораторной диагностике является разработка технологий для фенотипирования групп крови с использованием в качестве реагентов широко распространённых моноклональных антител и технологии КЗ на носимых устройствах. Вместе с тем на рынке часто отсутствуют готовые решения для включения интеллектуальных программных систем в повседневную работу лаборатории.

В обзоре рассмотрены различные примеры использования в лабораторной диагностике технологических систем, основанных на ИИ. Также в работе представлен библиометрический анализ научной литературы, касающейся распространения практики использования технологий КЗ, машинного обучения и ИИ в медицинских лабораториях на основании публикаций из базы данных PubMed за предшествующие 20 лет. Кроме того, в обзоре обсуждаются перспективы и ограничения для применения ИИ и КЗ в медицинских лабораториях и проведена оценка преимуществ внедрения в клиническую практику метода фенотипирования групп крови с использованием технологии ИИ на мобильных устройствах.

Участие авторов

Участие авторов: Трегуб П.П. — концепция и дизайн обзора, написание текста, составление списка литературы, статистическая обработка данных; Жемчугин Д.Е., Зубанов П.С. — написание текста, составление списка литературы, научное редактирование; Гольдберг А.С., Годков М.А., Акимкин В.Г. — написание текста, научное редактирование. Все соавторы — утверждение окончательного варианта статьи, ответственность за целостность всех частей статьи.

Финансирование

Финансирование. Исследование не имело спонсорской поддержки.

Конфликт интересов

Конфликт интересов. Авторы декларируют отсутствие явных и потенциальных конфликтов интересов в связи с публикацией данной статьи.

Поступила

Поступила: 21.02.2025 / Принята к печати: 11.03.2025 / Опубликована: 30.04.2025

Laboratory diagnostics plays one of the leading roles in modern medicine, providing doctors of clinical specialties with data for timely diagnosis, selection of tactics and methods of treatment. To ensure high efficiency and increase the accuracy of research, artificial intelligence technologies have recently been actively introduced into the practice of the laboratory service: computer vision, machine learning, deep learning, neural networks, data bank analysis. In laboratory diagnostics, these technologies are successfully used to automate and improve technological processes, including processing reaction results, cytomorphological images, and analysis of the obtained data. One of the promising areas for the implementation of artificial intelligence in laboratory diagnostics is the development of technologies for phenotyping blood groups using widely used monoclonal antibodies as reagents and computer vision technology on wearable devices. At the same time, there are often no ready-made solutions on the market for including intelligent software systems in the daily work of the laboratory. The review considers various examples of the use of technological systems based on artificial intelligence in laboratory diagnostics. The paper also presents a bibliometric analysis of scientific literature on the spread of computer vision, machine learning, and artificial intelligence technologies in medical laboratories based on publications from the Pubmed database over the past 20 years. In addition, the review discusses the prospects and limitations of using artificial intelligence and computer vision in medical laboratories and assesses the benefits of introducing the blood group phenotyping method into clinical practice using artificial intelligence technology on mobile devices.

Contribution of the authors: Tregub P.P. — research concept and design, writing the text, compiling of the list of literature, statistical data processing;Zhemchugin D.E., Zubanov P.S. — writing the text, compiling of the list of literature, editing; Goldberg A.S., Godkov M.A., Akimkin V.G. — writing the text, editing. All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.

Acknowledgment

Acknowledgment. The study had no sponsorship.

Conflict of interest

Conflict of interest. The authors declare no conflict of interest.

Received

Received: February 21, 2025 / Accepted: March 11, 2025 / Published: April 30, 2025

компьютерное зрениеискусственный интеллектдополненная реальностьинтернет вещейлабораторная диагностикаиммуногематологияобзорcomputer visionartificial intelligenceaugmented realityInternet of thingslaboratory diagnosticsimmunohematologyreviewReferencesPlebani M. The CCLM contribution to improvements in quality and patient safety. Clin. Chem. Lab. Med. 2013; 51(1): 39–46. https://doi.org/10.1515/cclm-2012-0094Plebani M. The CCLM contribution to improvements in quality and patient safety. Clin. Chem. Lab. Med. 2013; 51(1): 39–46. https://doi.org/10.1515/cclm-2012-0094Klatt E.C. Cognitive factors impacting patient understanding of laboratory test information. J. Pathol. Inform. 2023; 15: 100349. https://doi.org/10.1016/j.jpi.2023.100349Klatt E.C. Cognitive factors impacting patient understanding of laboratory test information. J. Pathol. Inform. 2023; 15: 100349. https://doi.org/10.1016/j.jpi.2023.100349Plebani M., Astion M.L., Barth J.H., Chen W., de Oliveira Galoro C.A., Escuer M.I., et al. Harmonization of quality indicators in laboratory medicine. A preliminary consensus. Clin. Chem. Lab. Med. 2014; 52(7): 951–8. https://doi.org/10.1515/cclm-2014-0142Plebani M., Astion M.L., Barth J.H., Chen W., de Oliveira Galoro C.A., Escuer M.I., et al. Harmonization of quality indicators in laboratory medicine. A preliminary consensus. Clin. Chem. Lab. Med. 2014; 52(7): 951–8. https://doi.org/10.1515/cclm-2014-0142Undru T.R., Uday U., Lakshmi J.T., Kaliappan A., Mallamgunta S., Nikhat S.S., et al. Integrating artificial intelligence for clinical and laboratory diagnosis – a review. Maedica (Bucur). 2022; 17(2): 420–6. https://doi.org/10.26574/maedica.2022.17.2.420Undru T.R., Uday U., Lakshmi J.T., Kaliappan A., Mallamgunta S., Nikhat S.S., et al. Integrating artificial intelligence for clinical and laboratory diagnosis – a review. Maedica (Bucur). 2022; 17(2): 420–6. https://doi.org/10.26574/maedica.2022.17.2.420Ronzio L., Cabitza F., Barbaro A., Banfi G. Has the flood entered the basement? A systematic literature review about machine learning in laboratory medicine. Diagnostics (Basel). 2021; 11(2): 372. https://doi.org/10.3390/diagnostics11020372Ronzio L., Cabitza F., Barbaro A., Banfi G. Has the flood entered the basement? A systematic literature review about machine learning in laboratory medicine. Diagnostics (Basel). 2021; 11(2): 372. https://doi.org/10.3390/diagnostics11020372Tsai E.R., Tintu A.N., Boucherie R.J., de Rijke Y.B., Schotman H.H.M., Demirtas D. Characterization of laboratory flow and performance for process improvements via application of process mining. Appl. Clin. Inform. 2023; 14(1): 144–52. https://doi.org/10.1055/a-1996-8479Tsai E.R., Tintu A.N., Boucherie R.J., de Rijke Y.B., Schotman H.H.M., Demirtas D. Characterization of laboratory flow and performance for process improvements via application of process mining. Appl. Clin. Inform. 2023; 14(1): 144–52. https://doi.org/10.1055/a-1996-8479Lindroth H., Nalaie K., Raghu R., Ayala I.N., Busch C., Bhattacharyya A., et al. Applied artificial intelligence in healthcare: a review of computer vision technology application in hospital settings. J. Imaging. 2024; 10(4): 81. https://doi.org/10.3390/jimaging10040081Lindroth H., Nalaie K., Raghu R., Ayala I.N., Busch C., Bhattacharyya A., et al. Applied artificial intelligence in healthcare: a review of computer vision technology application in hospital settings. J. Imaging. 2024; 10(4): 81. https://doi.org/10.3390/jimaging10040081Gao J., Yang Y., Lin P., Park D.S. Computer vision in healthcare applications. J. Healthc. Eng. 2018; 2018: 5157020. https://doi.org/10.1155/2018/5157020Gao J., Yang Y., Lin P., Park D.S. Computer vision in healthcare applications. J. Healthc. Eng. 2018; 2018: 5157020. https://doi.org/10.1155/2018/5157020Haymond S., McCudden C. Rise of the machines: artificial intelligence and the clinical laboratory. J. Appl. Lab. Med. 2021; 6(6): 1640–54. https://doi.org/10.1093/jalm/jfab075Haymond S., McCudden C. Rise of the machines: artificial intelligence and the clinical laboratory. J. Appl. Lab. Med. 2021; 6(6): 1640–54. https://doi.org/10.1093/jalm/jfab075Korchagin S., Zaychenkova E., Ershov E., Pishchev P., Vengerov Y. Image-based second opinion for blood typing. Health Inf. Sci. Syst. 2024; 12(1): 28. https://doi.org/10.1007/s13755-024-00289-4Korchagin S., Zaychenkova E., Ershov E., Pishchev P., Vengerov Y. Image-based second opinion for blood typing. Health Inf. Sci. Syst. 2024; 12(1): 28. https://doi.org/10.1007/s13755-024-00289-4Cadamuro J., Hillarp A., Unger A., von Meyer A., Bauçà J.M., Plekhanova O., et al. Presentation and formatting of laboratory results: a narrative review on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group "postanalytical phase" (WG-POST). Crit. Rev. Clin. Lab. Sci. 2021; 58(5): 329–53. https://doi.org/10.1080/10408363.2020.1867051Cadamuro J., Hillarp A., Unger A., von Meyer A., Bauçà J.M., Plekhanova O., et al. Presentation and formatting of laboratory results: a narrative review on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group "postanalytical phase" (WG-POST). Crit. Rev. Clin. Lab. Sci. 2021; 58(5): 329–53. https://doi.org/10.1080/10408363.2020.1867051Patel A.U., Shaker N., Mohanty S., Sharma S., Gangal S., Eloy C., et al. Cultivating clinical clarity through computer vision: a current perspective on whole slide imaging and artificial intelligence. Diagnostics (Basel). 2022; 12(8): 1778. https://doi.org/10.3390/diagnostics12081778Patel A.U., Shaker N., Mohanty S., Sharma S., Gangal S., Eloy C., et al. Cultivating clinical clarity through computer vision: a current perspective on whole slide imaging and artificial intelligence. Diagnostics (Basel). 2022; 12(8): 1778. https://doi.org/10.3390/diagnostics12081778Cadamuro J. Rise of the machines: the inevitable evolution of medicine and medical laboratories intertwining with artificial intelligence – a narrative review. Diagnostics (Basel). 2021; 11(8): 1399. https://doi.org/10.3390/diagnostics11081399Cadamuro J. Rise of the machines: the inevitable evolution of medicine and medical laboratories intertwining with artificial intelligence – a narrative review. Diagnostics (Basel). 2021; 11(8): 1399. https://doi.org/10.3390/diagnostics11081399Association for the Advancement of Artificial Intelligence. Available at: https://aaai.org/Association for the Advancement of Artificial Intelligence. Available at: https://aaai.org/Zhou S., Chen B., Fu E.S., Yan H. Computer vision meets microfluidics: a label-free method for high-throughput cell analysis. Microsyst. Nanoeng. 2023; 9: 116. https://doi.org/10.1038/s41378-023-00562-8Zhou S., Chen B., Fu E.S., Yan H. Computer vision meets microfluidics: a label-free method for high-throughput cell analysis. Microsyst. Nanoeng. 2023; 9: 116. https://doi.org/10.1038/s41378-023-00562-8Syed T.A., Siddiqui M.S., Abdullah H.B., Jan S., Namoun A., Alzahrani A., et al. In-depth review of augmented reality: tracking technologies, development tools, AR displays, collaborative AR, and security concerns. Sensors (Basel). 2022; 23(1): 146. https://doi.org/10.3390/s23010146Syed T.A., Siddiqui M.S., Abdullah H.B., Jan S., Namoun A., Alzahrani A., et al. In-depth review of augmented reality: tracking technologies, development tools, AR displays, collaborative AR, and security concerns. Sensors (Basel). 2022; 23(1): 146. https://doi.org/10.3390/s23010146Rupp N., Peschke K., Köppl M., Drissner D., Zuchner T. Establishment of low-cost laboratory automation processes using AutoIt and 4-axis robots. SLAS Technol. 2022; 27(5): 312–8. https://doi.org/10.1016/j.slast.2022.07.001Rupp N., Peschke K., Köppl M., Drissner D., Zuchner T. Establishment of low-cost laboratory automation processes using AutoIt and 4-axis robots. SLAS Technol. 2022; 27(5): 312–8. https://doi.org/10.1016/j.slast.2022.07.001Manickam P., Mariappan S.A., Murugesan S.M., Hansda S., Kaushik A., Shinde R., et al. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors (Basel). 2022; 12(8): 562. https://doi.org/10.3390/bios12080562Manickam P., Mariappan S.A., Murugesan S.M., Hansda S., Kaushik A., Shinde R., et al. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors (Basel). 2022; 12(8): 562. https://doi.org/10.3390/bios12080562Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019; 25(1): 44–56. https://doi.org/10.1038/s41591-018-0300-7Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019; 25(1): 44–56. https://doi.org/10.1038/s41591-018-0300-7Iqbal J., Cortés Jaimes D.C., Makineni P., Subramani S., Hemaida S., Thugu T.R., et al. Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus. 2023; 15(9): e44658. https://doi.org/10.7759/cureus.44658Iqbal J., Cortés Jaimes D.C., Makineni P., Subramani S., Hemaida S., Thugu T.R., et al. Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus. 2023; 15(9): e44658. https://doi.org/10.7759/cureus.44658Wen X., Leng P., Wang J., Yang G., Zu R., Jia X., et al. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics. 2022; 23(1): 387. https://doi.org/10.1186/s12859-022-04926-1Wen X., Leng P., Wang J., Yang G., Zu R., Jia X., et al. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics. 2022; 23(1): 387. https://doi.org/10.1186/s12859-022-04926-1Stafford I.S., Kellermann M., Mossotto E., Beattie R.M., MacArthur B.D., Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit. Med. 2020; 3: 30. https://doi.org/10.1038/s41746-020-0229-3Stafford I.S., Kellermann M., Mossotto E., Beattie R.M., MacArthur B.D., Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit. Med. 2020; 3: 30. https://doi.org/10.1038/s41746-020-0229-3Wald N.J., Cuckle H.S., Densem J.W., Nanchahal K., Royston P., Chard T., et al. Maternal serum screening for Down’s syndrome in early pregnancy. BMJ. 1988; 297(6653): 883–7. https://doi.org/10.1136/bmj.297.6653.883Wald N.J., Cuckle H.S., Densem J.W., Nanchahal K., Royston P., Chard T., et al. Maternal serum screening for Down’s syndrome in early pregnancy. BMJ. 1988; 297(6653): 883–7. https://doi.org/10.1136/bmj.297.6653.883Hadlow N.C., Rothacker K.M., Wardrop R., Brown S.J., Lim E.M., Walsh J.P. The relationship between TSH and free T4 in a large population is complex and nonlinear and differs by age and sex. J. Clin. Endocrinol. Metab. 2013; 98(7): 2936–43. https://doi.org/10.1210/jc.2012-4223Hadlow N.C., Rothacker K.M., Wardrop R., Brown S.J., Lim E.M., Walsh J.P. The relationship between TSH and free T4 in a large population is complex and nonlinear and differs by age and sex. J. Clin. Endocrinol. Metab. 2013; 98(7): 2936–43. https://doi.org/10.1210/jc.2012-4223Asar T.O., Ragab M. Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning. Sci. Rep. 2024; 14(1): 21755. https://doi.org/10.1038/s41598-024-72900-3Asar T.O., Ragab M. Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning. Sci. Rep. 2024; 14(1): 21755. https://doi.org/10.1038/s41598-024-72900-3Bunch D.R., Durant T.J., Rudolf J.W. Artificial intelligence applications in clinical chemistry. Clin. Lab. Med. 2023; 43(1): 47–69. https://doi.org/10.1016/j.cll.2022.09.005Bunch D.R., Durant T.J., Rudolf J.W. Artificial intelligence applications in clinical chemistry. Clin. Lab. Med. 2023; 43(1): 47–69. https://doi.org/10.1016/j.cll.2022.09.005van Eekelen L., Litjens G., Hebeda K.M. Artificial intelligence in bone marrow histological diagnostics: potential applications and challenges. Pathobiology. 2024; 91(1): 8–17. https://doi.org/10.1159/000529701van Eekelen L., Litjens G., Hebeda K.M. Artificial intelligence in bone marrow histological diagnostics: potential applications and challenges. Pathobiology. 2024; 91(1): 8–17. https://doi.org/10.1159/000529701Kimura K., Ai T., Horiuchi Y., Matsuzaki A., Nishibe K., Marutani S., et al. Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen. Sci. Rep. 2021; 11(1): 3367. https://doi.org/10.1038/s41598-021-82826-9Kimura K., Ai T., Horiuchi Y., Matsuzaki A., Nishibe K., Marutani S., et al. Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen. Sci. Rep. 2021; 11(1): 3367. https://doi.org/10.1038/s41598-021-82826-9Walter C., Weissert C., Gizewski E., Burckhardt I., Mannsperger H., Hänselmann S., et al. Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures. J. Clin. Microbiol. 2024; 62(4): e0087623. https://doi.org/10.1128/jcm.00876-23Walter C., Weissert C., Gizewski E., Burckhardt I., Mannsperger H., Hänselmann S., et al. Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures. J. Clin. Microbiol. 2024; 62(4): e0087623. https://doi.org/10.1128/jcm.00876-23Smith K.P., Kirby J.E. Image analysis and artificial intelligence in infectious disease diagnostics. Clin. Microbiol. Infect. 2020; 26(10): 1318–23. https://doi.org/10.1016/j.cmi.2020.03.012Smith K.P., Kirby J.E. Image analysis and artificial intelligence in infectious disease diagnostics. Clin. Microbiol. Infect. 2020; 26(10): 1318–23. https://doi.org/10.1016/j.cmi.2020.03.012Mathison B.A., Kohan J.L., Walker J.F., Smith R.B., Ardon O., Couturier M.R. Detection of intestinal protozoa in trichrome-stained stool specimens by use of a deep convolutional neural network. J. Clin. Microbiol. 2020; 58(6): e02053-19. https://doi.org/10.1128/JCM.02053-19Mathison B.A., Kohan J.L., Walker J.F., Smith R.B., Ardon O., Couturier M.R. Detection of intestinal protozoa in trichrome-stained stool specimens by use of a deep convolutional neural network. J. Clin. Microbiol. 2020; 58(6): e02053-19. https://doi.org/10.1128/JCM.02053-19Chowdhury N.I., Smith T.L., Chandra R.K., Turner J.H. Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks. Int. Forum Allergy Rhinol. 2019; 9(1): 46–52. https://doi.org/10.1002/alr.22196Chowdhury N.I., Smith T.L., Chandra R.K., Turner J.H. Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks. Int. Forum Allergy Rhinol. 2019; 9(1): 46–52. https://doi.org/10.1002/alr.22196Grigorev G.V., Lebedev A.V., Wang X., Qian X., Maksimov G.V., Lin L. Advances in microfluidics for single red blood cell analysis. Biosensors (Basel). 2023; 13(1): 117. https://doi.org/10.3390/bios13010117Grigorev G.V., Lebedev A.V., Wang X., Qian X., Maksimov G.V., Lin L. Advances in microfluidics for single red blood cell analysis. Biosensors (Basel). 2023; 13(1): 117. https://doi.org/10.3390/bios13010117Seyedi S.S., Parvin P., Jafargholi A., Hashemi N., Tabatabaee S.M., Abbasian A., et al. Spectroscopic properties of various blood antigens/antibodies. Biomed. Opt. Express. 2020; 11(4): 2298–312. https://doi.org/10.1364/BOE.387112Seyedi S.S., Parvin P., Jafargholi A., Hashemi N., Tabatabaee S.M., Abbasian A., et al. Spectroscopic properties of various blood antigens/antibodies. Biomed. Opt. Express. 2020; 11(4): 2298–312. https://doi.org/10.1364/BOE.387112Sheng N., Liu L., Liu H. Quantitative determination of agglutination based on the automatic hematology analyzer and the clinical significance of the erythrocyte-specific antibody. Clin. Chim. Acta. 2020; 510: 21–5. https://doi.org/10.1016/j.cca.2020.06.042Sheng N., Liu L., Liu H. Quantitative determination of agglutination based on the automatic hematology analyzer and the clinical significance of the erythrocyte-specific antibody. Clin. Chim. Acta. 2020; 510: 21–5. https://doi.org/10.1016/j.cca.2020.06.042Li H.Y., Guo K. Blood group testing. Front. Med. (Lausanne). 2022; 9: 827619. https://doi.org/10.3389/fmed.2022.827619Li H.Y., Guo K. Blood group testing. Front. Med. (Lausanne). 2022; 9: 827619. https://doi.org/10.3389/fmed.2022.827619Aysola A., Wheeler L., Brown R., Denham R., Colavecchia C., Pavenski K., et al. Multi-center evaluation of the automated immunohematology instrument, the ORTHO VISION analyzer. Lab. Med. 2017; 48(1): 29–38. https://doi.org/10.1093/labmed/lmw061Aysola A., Wheeler L., Brown R., Denham R., Colavecchia C., Pavenski K., et al. Multi-center evaluation of the automated immunohematology instrument, the ORTHO VISION analyzer. Lab. Med. 2017; 48(1): 29–38. https://doi.org/10.1093/labmed/lmw061Bhagwat S.N., Sharma J.H., Jose J., Modi C.J. Comparison between conventional and automated techniques for blood grouping and crossmatching: experience from a tertiary care centre. J. Lab. Physicians. 2015; 7(2): 96–102. https://doi.org/10.4103/0974-2727.163130Bhagwat S.N., Sharma J.H., Jose J., Modi C.J. Comparison between conventional and automated techniques for blood grouping and crossmatching: experience from a tertiary care centre. J. Lab. Physicians. 2015; 7(2): 96–102. https://doi.org/10.4103/0974-2727.163130Moulds M.K. Review: monoclonal reagents and detection of unusual or rare phenotypes or antibodies. Immunohematology. 2006; 22(2): 52–63.Moulds M.K. Review: monoclonal reagents and detection of unusual or rare phenotypes or antibodies. Immunohematology. 2006; 22(2): 52–63.Voak D. Monoclonal blood group antibodies. Beitr. Infusionsther. 1989; 24: 200–13.Voak D. Monoclonal blood group antibodies. Beitr. Infusionsther. 1989; 24: 200–13.Ratajczak K., Sklodowska-Jaros K., Kalwarczyk E., Michalski J.A., Jakiela S., Stobiecka M. Effective optical image assessment of cellulose paper immunostrips for blood typing. Int. J. Mol. Sci. 2022; 23(15): 8694. https://doi.org/10.3390/ijms23158694Ratajczak K., Sklodowska-Jaros K., Kalwarczyk E., Michalski J.A., Jakiela S., Stobiecka M. Effective optical image assessment of cellulose paper immunostrips for blood typing. Int. J. Mol. Sci. 2022; 23(15): 8694. https://doi.org/10.3390/ijms23158694Ding S., Duan S., Chen Y., Xie J., Tian J., Li Y., et al. Centrifugal microfluidic platform with digital image analysis for parallel red cell antigen typing. Talanta. 2023; 252: 123856. https://doi.org/10.1016/j.talanta.2022.123856Ding S., Duan S., Chen Y., Xie J., Tian J., Li Y., et al. Centrifugal microfluidic platform with digital image analysis for parallel red cell antigen typing. Talanta. 2023; 252: 123856. https://doi.org/10.1016/j.talanta.2022.123856Hyvärinen K., Haimila K., Moslemi C., Biobank B.S., Olsson M.L., Ostrowski S.R., et al. A machine-learning method for biobank-scale genetic prediction of blood group antigens. PLoS Comput. Biol. 2024; 20(3): e1011977. https://doi.org/10.1371/journal.pcbi.1011977Hyvärinen K., Haimila K., Moslemi C., Biobank B.S., Olsson M.L., Ostrowski S.R., et al. A machine-learning method for biobank-scale genetic prediction of blood group antigens. PLoS Comput. Biol. 2024; 20(3): e1011977. https://doi.org/10.1371/journal.pcbi.1011977Korchagin S.A., Zaychenkova E.E., Sharapov D.A., Ershov E.I., Butorin U.V., Vengerov U.U. An algorithm of blood typing using serological plate images. Comput. Opt. 2023; 47(6): 958–67. https://doi.org/10.18287/2412-6179-CO-1339Korchagin S.A., Zaychenkova E.E., Sharapov D.A., Ershov E.I., Butorin U.V., Vengerov U.U. An algorithm of blood typing using serological plate images. Comput. Opt. 2023; 47(6): 958–67. https://doi.org/10.18287/2412-6179-CO-1339Pfeil J., Nechyporenko A., Frohme M., Hufert F.T., Schulze K. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics. 2022; 23(1): 65. https://doi.org/10.1186/s12859-022-04602-4Pfeil J., Nechyporenko A., Frohme M., Hufert F.T., Schulze K. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics. 2022; 23(1): 65. https://doi.org/10.1186/s12859-022-04602-4Blatter T.U., Witte H., Nakas C.T., Leichtle A.B. Big data in laboratory medicine – FAIR quality for AI? Diagnostics (Basel). 2022; 12(8): 1923. https://doi.org/10.3390/diagnostics12081923Blatter T.U., Witte H., Nakas C.T., Leichtle A.B. Big data in laboratory medicine – FAIR quality for AI? Diagnostics (Basel). 2022; 12(8): 1923. https://doi.org/10.3390/diagnostics12081923Kulikowski C.A. Historical roots of international biomedical and health informatics: the road to IFIP-TC4 and IMIA through cybernetic medicine and the Elsinore meetings. Yearb. Med. Inform. 2017; 26(1): 257–62. https://doi.org/10.15265/IY-2017-001Kulikowski C.A. Historical roots of international biomedical and health informatics: the road to IFIP-TC4 and IMIA through cybernetic medicine and the Elsinore meetings. Yearb. Med. Inform. 2017; 26(1): 257–62. https://doi.org/10.15265/IY-2017-001Kozak J., Fel S. How sociodemographic factors relate to trust in artificial intelligence among students in Poland and the United Kingdom. Sci. Rep. 2024; 14(1): 28776. https://doi.org/10.1038/s41598-024-80305-5Kozak J., Fel S. How sociodemographic factors relate to trust in artificial intelligence among students in Poland and the United Kingdom. Sci. Rep. 2024; 14(1): 28776. https://doi.org/10.1038/s41598-024-80305-5Paranjape K., Schinkel M., Hammer R.D., Schouten B., Nannan Panday R.S., Elbers P.W.G., et al. The value of artificial intelligence in laboratory medicine. Am. J. Clin. Pathol. 2021; 155(6): 823–31. https://doi.org/10.1093/ajcp/aqaa170Paranjape K., Schinkel M., Hammer R.D., Schouten B., Nannan Panday R.S., Elbers P.W.G., et al. The value of artificial intelligence in laboratory medicine. Am. J. Clin. Pathol. 2021; 155(6): 823–31. https://doi.org/10.1093/ajcp/aqaa170Herman D.S., Rhoads D.D., Schulz W.L., Durant T.J.S. Artificial intelligence and mapping a new direction in laboratory medicine: a review. Clin. Chem. 2021; 67(11): 1466–82. https://doi.org/10.1093/clinchem/hvab165Herman D.S., Rhoads D.D., Schulz W.L., Durant T.J.S. Artificial intelligence and mapping a new direction in laboratory medicine: a review. Clin. Chem. 2021; 67(11): 1466–82. https://doi.org/10.1093/clinchem/hvab165

The authors declare that there are no conflicts of interest present.