Prospects for the implementation of artificial intelligence and computer vision technologies in laboratory medicine (literature review)

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. The study had no sponsorship.

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

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

1. Plebani 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-2012-0094

2. Klatt E.C. Cognitive factors impacting patient understanding of laboratory test information. J. Pathol. Inform. 2023; 15: 100349. https://doi.org/10.1016/j.jpi.2023.100349

3. Plebani 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-2014-0142

4. Undru 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.2022.17.2.420

5. Ronzio 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/diagnostics11020372

6. Tsai 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-1996-8479

7. Lindroth 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/jimaging10040081

8. Gao J., Yang Y., Lin P., Park D.S. Computer vision in healthcare applications. J. Healthc. Eng. 2018; 2018: 5157020. https://doi.org/10.1155/2018/5157020

9. Haymond 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/jfab075

10. Korchagin 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/s13755-024-00289-4

11. Cadamuro 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/10.1080/10408363.2020.1867051

12. Patel 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/diagnostics12081778

13. Cadamuro 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/diagnostics11081399

14. Association for the Advancement of Artificial Intelligence. Available at: https://aaai.org/

15. 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/s41378-023-00562-8

16. Syed 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/s23010146

17. Rupp 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.2022.07.001

18. Manickam 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/bios12080562

19. Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019; 25(1): 44–56. https://doi.org/10.1038/s41591-018-0300-7

20. Iqbal 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.44658

21. Wen 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/s12859-022-04926-1

22. Stafford 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/s41746-020-0229-3

23. Wald 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.297.6653.883

24. Hadlow 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.2012-4223

25. Asar 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/s41598-024-72900-3

26. Bunch 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.2022.09.005

27. van 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/10.1159/000529701

28. Kimura 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/s41598-021-82826-9

29. Walter 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.00876-23

30. Smith 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.2020.03.012

31. Mathison 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.02053-19

32. Chowdhury 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.22196

33. Grigorev 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/bios13010117

34. Seyedi 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.387112

35. Sheng 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.2020.06.042

36. Li H.Y., Guo K. Blood group testing. Front. Med. (Lausanne). 2022; 9: 827619. https://doi.org/10.3389/fmed.2022.827619

37. Aysola 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/lmw061

38. Bhagwat 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/10.4103/0974-2727.163130

39. Moulds M.K. Review: monoclonal reagents and detection of unusual or rare phenotypes or antibodies. Immunohematology. 2006; 22(2): 52–63.

40. Voak D. Monoclonal blood group antibodies. Beitr. Infusionsther. 1989; 24: 200–13.

41. 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/ijms23158694

42. Ding 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.2022.123856

43. Hyvärinen 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.1011977

44. Korchagin 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-1339

45. Pfeil 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/s12859-022-04602-4

46. Blatter 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/diagnostics12081923

47. Kulikowski 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-2017-001

48. Kozak 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/s41598-024-80305-5

49. Paranjape 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/aqaa170

50. Herman 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