Ann Lab Med.  2025 Jan;45(1):22-35. 10.3343/alm.2024.0354.

Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact

• You J
• Seok HS
• Kim S
• Shin H

• KMID: 2563852
• DOI: http://doi.org/10.3343/alm.2024.0354

Abstract

Machine learning (ML) is currently being widely studied and applied in data analysis and prediction in various fields, including laboratory medicine. To comprehensively evaluate the application of ML in laboratory medicine, we reviewed the literature on ML applications in laboratory medicine published between February 2014 and March 2024. A PubMed search using a search string yielded 779 articles on the topic, among which 144 articles were selected for this review. These articles were analyzed to extract and categorize related fields within laboratory medicine, research objectives, specimen types, data types, ML models, evaluation metrics, and sample sizes. Sankey diagrams and pie charts were used to illustrate the relationships between categories and the proportions within each category. We found that most studies involving the application of ML in laboratory medicine were designed to improve efficiency through automation or expand the roles of clinical laboratories. The most common ML models used are convolutional neural networks, multilayer perceptrons, and tree-based models, which are primarily selected based on the type of input data. Our findings suggest that, as the technology evolves, ML will rise in prominence in laboratory medicine as a tool for expanding research activities. Nonetheless, expertise in ML applications should be improved to effectively utilize this technology.

Keyword

Artificial intelligence; Clinical laboratory tests; Laboratory medicine; Machine learning

Figure

• 

  Fig. 1 Sankey diagram showing the relationships among representative laboratory medicine fields, their main objectives, and ML models identified through a literature review of studies applying ML in laboratory medicine from February 2014 to April 2024. Abbreviations: AI, artificial intelligence; CNN, convolutional neural network; DNN, deep neural network; LR, logistic regression; ML, machine learning; MLP, multilayer perceptron; N/S, not specified; SVM, support vector machine.

• 

  Fig. 2 Pie charts showing the proportions of ML models used in various laboratory medicine fields based on a literature review of laboratory medicine studies involving ML applications from February 2014 to April 2024. Numbers in parentheses indicate the number of published articles related to ML in each field. The frequencies at which various ML models were used in (A) diagnostic hematology, (B) clinical chemistry, (C) clinical microbiology, (D) molecular diagnostics, (E) transfusion medicine, and (F) diagnostic immunology are shown. Abbreviations: CNN, convolutional neural network; DBN, deep belief network; DNN, deep neural network; HCA, hierarchical cluster analysis; LLM, large language model; LR, logistic regression; LSTM, long short-term memory; ML, machine learning; MLP, multilayer perceptron; N/S, not specified; PLS-DA, partial least squares-discriminant analysis; RF, random forest; RNN, recurrent neural network; SVM, support vector machine; UMAP, uniform manifold approximation and projection; XGB, extreme gradient boosting.

• 

  Fig. 3 Sankey diagram showing relationships among the year, best ML models, and data type based on a literature review of studies applying ML in laboratory medicine from February 2014 to April 2024. Abbreviations: CNN, convolutional neural network; DNN, deep neural network; LR, logistic regression; ML, machine learning; MLP, multilayer perceptron; N/A, not applicable; N/S, not specified; SVM, support vector machine.

• 

  Fig. 4 Features of different ML models. (A) LR based on the sigmoid function, expressed as a probability value between 0 and 1, divided by the threshold. (B) An example of sample classification using a hyperplane and an SVM. (C) An MLP comprising an input layer, a hidden layer, and an output layer composed of connected perceptrons. (D) A DNN comprises more hidden layers than an MLP and is an extension of an MLP. (E) A CNN comprises convolution layers and is primarily used for image processing. (F) A DT-based model follows decision rules in a tree structure. Abbreviations: C, class; CNN, convolutional neural network; DNN, deep neural network; DT, decision tree; LR, logistic regression; ML, machine learning; MLP, multilayer perceptron; SVM, support vector machine; Q, question.

Reference

References

1. Benjamens S, Dhunnoo P, Meskó B. 2020; The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 3:118. DOI: 10.1038/s41746-020-00324-0. PMID: 32984550. PMCID: PMC7486909. PMID: 57ebc19d581944ad8bbd6b42f5de1019.

2. Cui M, Zhang DY. 2021; Artificial intelligence and computational pathology. Lab Invest. 101:412–22. DOI: 10.1038/s41374-020-00514-0. PMID: 33454724. PMCID: PMC7811340.

3. Rabbani N, Kim GYE, Suarez CJ, Chen JH. 2022; Applications of machine learning in routine laboratory medicine: current state and future directions. Clin Biochem. 103:1–7. DOI: 10.1016/j.clinbiochem.2022.02.011. PMID: 35227670. PMCID: PMC9007900.

4. Khan AI, Khan M, Khan R. 2023; Artificial intelligence in point-of-care testing. Ann Lab Med. 43:401–7. DOI: 10.3343/alm.2023.43.5.401. PMID: 37080740. PMCID: PMC10151281.

5. Duan X, Zhang M, Liu Y, Zheng W, Lim CY, Kim S, et al. 2024; Next-generation patient-based real-time quality control models. Ann Lab Med. 44:385–91. DOI: 10.3343/alm.2024.0053. PMID: 38835211. PMCID: PMC11169771.

6. Pillay TS. 2021; Artificial intelligence in pathology and laboratory medicine. J Clin Pathol. 74:407–8. DOI: 10.1136/jclinpath-2021-207682. PMID: 34031137.

7. Niazi MKK, Parwani AV, Gurcan MN. 2019; Digital pathology and artificial intelligence. Lancet Oncol. 20:e253–61. DOI: 10.1016/S1470-2045(19)30154-8. PMID: 31044723. PMCID: PMC8711251.

8. Korean Society for Laboratory Medicine. 2021. Laboratory medicine. 6th ed. Panmun Education;Seoula: DOI: 10.1016/s0272-2712(21)00062-7.

9. Logical Observation Identifiers Names and Codes Committee. LOINC specimen type. https://loinc.org/66746-9.

10. Redmon J, Divvala S, Girshick R, Farhadi A. 2016; You only look once: unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 779–88. DOI: 10.1109/CVPR.2016.91.

11. Bogart S. SankeyMATIC. https://sankeymatic.com/.

12. Hunter JD. 2007; Matplotlib: a 2D graphics environment. Comput Sci Eng. 9:90–5. DOI: 10.1109/MCSE.2007.55.

13. Xie J, Wang Q. 2020; Benchmarking machine learning algorithms on blood glucose prediction for type I diabetes in comparison with classical time-series models. IEEE Trans Biomed Eng. 67:3101–24. DOI: 10.1109/TBME.2020.2975959. PMID: 32091990.

14. Zhu T, Li K, Herrero P, Georgiou P. 2023; Personalized blood glucose prediction for type 1 diabetes using evidential deep learning and meta-learning. IEEE Trans Biomed Eng. 70:193–204. DOI: 10.1109/TBME.2022.3187703. PMID: 35776825.

15. Snowden SG, Korosi A, de Rooij SR, Koulman A. 2020; Combining lipidomics and machine learning to measure clinical lipids in dried blood spots. Metabolomics. 16:83. DOI: 10.1007/s11306-020-01703-0. PMID: 32710150. PMCID: PMC7381462.

16. Müller M, Sägesser N, Keller PM, Arampatzis S, Steffens B, Ehrhard S, et al. 2022; Urine flow cytometry parameter cannot safely predict contamination of urine-a cohort study of a Swiss emergency department using machine learning techniques. Diagnostics (Basel). 12:1008. DOI: 10.3390/diagnostics12041008. PMID: 35454055. PMCID: PMC9025120. PMID: 6b6f06dce1944d35a33cbd7e5dff0673.

17. Zhou R, Liang YF, Cheng HL, Wang W, Huang DW, Wang Z, et al. 2022; A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory. Clin Chem Lab Med. 60:1984–92. DOI: 10.1515/cclm-2021-1171. PMID: 34963042.

18. Seok HS, Choi Y, Yu S, Shin KH, Kim S, Shin H. 2024; Machine learning-based delta check method for detecting misidentification errors in tumor marker tests. Clin Chem Lab Med. 62:1421–32. DOI: 10.1515/cclm-2023-1185. PMID: 38095534.

19. Wang H, Wang H, Zhang J, Li X, Sun C, Zhang Y. 2021; Using machine learning to develop an autoverification system in a clinical biochemistry laboratory. Clin Chem Lab Med. 59:883–91. DOI: 10.1515/cclm-2020-0716. PMID: 33554565.

20. Demirci F, Akan P, Kume T, Sisman AR, Erbayraktar Z, Sevinc S. 2016; Artificial neural network approach in laboratory test reporting: learning algorithms. Am J Clin Pathol. 146:227–37. DOI: 10.1093/ajcp/aqw104. PMID: 27473741.

21. Ialongo C, Pieri M, Bernardini S. 2017; Artificial neural network for total laboratory automation to improve the management of sample dilution: smart automation for clinical laboratory timeliness. SLAS Technol. 22:44–9. DOI: 10.1177/2211068216636635. PMID: 26956577.

22. Dauwalder O, Michel A, Eymard C, Santos K, Chanel L, Luzzati A, et al. 2021; Use of artificial intelligence for tailored routine urine analyses. Clin Microbiol Infect. 27:1168.e1–6. DOI: 10.1016/j.cmi.2020.09.056. PMID: 33038526.

23. Wilkes EH, Emmett E, Beltran L, Woodward GM, Carling RS. 2020; A machine learning approach for the automated interpretation of plasma amino acid profiles. Clin Chem. 66:1210–8. DOI: 10.1093/clinchem/hvaa134. PMID: 32870990.

24. Stevenson E, Walsh C, Hibberd L. 2024; Can artificial intelligence replace biochemists? A study comparing interpretation of thyroid function test results by ChatGPT and Google Bard to practising biochemists. Ann Clin Biochem. 61:143–9. DOI: 10.1177/00045632231203473. PMID: 37699796.

25. Del Ben F, Da Col G, Cobârzan D, Turetta M, Rubin D, Buttazzi P, et al. 2023; A fully interpretable machine learning model for increasing the effectiveness of urine screening. Am J Clin Pathol. 160:620–32. DOI: 10.1093/ajcp/aqad099. PMID: 37658807. PMCID: PMC10691191.

26. Lee KS, Lim HJ, Kim K, Park YG, Yoo JW, Yong D. 2022; Rapid bacterial detection in urine using laser scattering and deep learning analysis. Microbiol Spectr. 10:e0176921. DOI: 10.1128/spectrum.01769-21. PMID: 35234514. PMCID: PMC8941854. PMID: a6f841e3750c4996a53c7118af440319.

27. Wang HY, Hung CC, Chen CH, Lee TY, Huang KY, Ning HC, et al. 2019; Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach. Sci Rep. 9:11074. DOI: 10.1038/s41598-019-47361-8. PMID: 31423009. PMCID: PMC6698480. PMID: 3428a47f1e9049b480633dbccf66043a.

28. Avci D, Leblebicioglu MK, Poyraz M, Dogantekin E. 2014; A new method based on adaptive discrete wavelet entropy energy and neural network classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling. J Med Syst. 38:7. DOI: 10.1007/s10916-014-0007-3. PMID: 24493072.

29. Vogado L, Veras R, Aires K, Araújo F, Silva R, Ponti M, et al. 2021; Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model. Sensors (Basel). 21:2989. DOI: 10.3390/s21092989. PMID: 33923209. PMCID: PMC8123151. PMID: 0dd7b4abd6df4f9aaddd3b57965739cf.

30. Jha KK, Dutta HS. 2019; Mutual information based hybrid model and deep learning for acute lymphocytic leukemia detection in single cell blood smear images. Comput Methods Programs Biomed. 179:104987. DOI: 10.1016/j.cmpb.2019.104987. PMID: 31443862.

31. Sampathila N, Chadaga K, Goswami N, Chadaga RP, Pandya M, Prabhu S, et al. 2022; Customized deep learning classifier for detection of acute lymphoblastic leukemia using blood smear images. Healthcare (Basel). 10:1812. DOI: 10.3390/healthcare10101812. PMID: 36292259. PMCID: PMC9601337.

32. Wang D, Hwang M, Jiang WC, Ding K, Chang HC, Hwang KS. 2021; A deep learning method for counting white blood cells in bone marrow images. BMC Bioinformatics. 22(S5):94. DOI: 10.1186/s12859-021-04003-z. PMID: 34749635. PMCID: PMC8576964. PMID: de6a87efa12f47cbb4c7d1d6e335f8ff.

33. Cheuque C, Querales M, León R, Salas R, Torres R. 2022; An efficient multi-level convolutional neural network approach for white blood cells classification. Diagnostics (Basel). 12:248. DOI: 10.3390/diagnostics12020248. PMID: 35204339. PMCID: PMC8871319. PMID: 309c20119d984b98891af3827d918d29.

34. Xing Y, Liu X, Dai J, Ge X, Wang Q, Hu Z, et al. 2023; Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood. BMC Med Inform Decis Mak. 23:50. DOI: 10.1186/s12911-023-02153-z. PMID: 36991420. PMCID: PMC10061886. PMID: daf79a24c0634d5784184e0fb1f25843.

35. Tamang T, Baral S, Paing MP. 2022; Classification of white blood cells: a comprehensive study using transfer learning based on convolutional neural networks. Diagnostics (Basel). 12:2903. DOI: 10.3390/diagnostics12122903. PMID: 36552910. PMCID: PMC9777002. PMID: 81d2d9168b8c48c8bef0fbeba198f0f2.

36. Kihm A, Kaestner L, Wagner C, Quint S. 2018; Classification of red blood cell shapes in flow using outlier tolerant machine learning. PLoS Comput Biol. 14:e1006278. DOI: 10.1371/journal.pcbi.1006278. PMID: 29906283. PMCID: PMC6021115. PMID: 093be896b681457f985b21d791e6475c.

37. Douglass PM, O'Connor T, Javidi B. 2022; Automated sickle cell disease identification in human red blood cells using a lensless single random phase encoding biosensor and convolutional neural networks. Opt Express. 30:35965–77. DOI: 10.1364/OE.469199. PMID: 36258535.

38. de Haan K, Ceylan Koydemir H, Rivenson Y, Tseng D, Van Dyne E, Bakic L, et al. 2020; Automated screening of sickle cells using a smartphone-based microscope and deep learning. NPJ Digit Med. 3:76. DOI: 10.1038/s41746-020-0282-y. PMID: 32509973. PMCID: PMC7244537. PMID: e595bac286b64f02bb247b171d271337.

39. Ahn D, Lee J, Moon S, Park T. 2018; Human-level blood cell counting on lens-free shadow images exploiting deep neural networks. Analyst. 143:5380–7. DOI: 10.1039/C8AN01056K. PMID: 30280723.

40. Huang X, Jiang Y, Liu X, Xu H, Han Z, Rong H, et al. 2016; Machine learning based single-frame super-resolution processing for lensless blood cell counting. Sensors (Basel). 16:1836. DOI: 10.3390/s16111836. PMID: 27827837. PMCID: PMC5134495. PMID: 4a72cecd1c61448faedfc489aa8a4cd2.

41. Alam MM, Islam MT. 2019; Machine learning approach of automatic identification and counting of blood cells. Healthc Technol Lett. 6:103–8. DOI: 10.1049/htl.2018.5098. PMID: 31531224. PMCID: PMC6718065. PMID: f63abfbc49eb41b290cd2b216967c0e9.

42. Zini G, Mancini F, Rossi E, Landucci S, d'Onofrio G. 2023; Artificial intelligence and the blood film: performance of the MC-80 digital morphology analyzer in samples with neoplastic and reactive cell types. Int J Lab Hematol. 45:881–9. DOI: 10.1111/ijlh.14160. PMID: 37641457.

43. Epah J, Gülec I, Winter S, Dörr J, Geisen C, Haecker E, et al. 2022; From unit to dose: a machine learning approach for precise prediction of hemoglobin and iron content in individual packed red blood cell units. Adv Sci (Weinh). 9:e2204077. DOI: 10.1002/advs.202204077. PMID: 36333123. PMCID: PMC9798979. PMID: d8cf21a3212e476db93119b61a5614a8.

44. Larpant N, Niamsi W, Noiphung J, Chanakiat W, Sakuldamrongpanich T, Kittichai V, et al. 2022; Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation. Anal Chim Acta. 1207:339807. DOI: 10.1016/j.aca.2022.339807. PMID: 35491041.

45. Roux-Dalvai F, Gotti C, Leclercq M, Hélie MC, Boissinot M, Arrey TN, et al. 2019; Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning. Mol Cell Proteomics. 18:2492–505. DOI: 10.1074/mcp.TIR119.001559. PMID: 31585987. PMCID: PMC6885708.

46. Amano M, Mai DT, Sun G, Vu TN, Hoi LT, Hoa NT, et al. 2022; Deep learning approach for classifying bacteria types using morphology of bacterial colony. Annu Int Conf IEEE Eng Med Biol Soc. 2022:2165–8. DOI: 10.1109/EMBC48229.2022.9870986. PMID: 36086561.

47. Rajaonison A, Le Page S, Maurin T, Chaudet H, Raoult D, Baron SA, et al. 2022; Antilogic, a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species. Clin Microbiol Infect. 28:1286.e1–8. DOI: 10.1016/j.cmi.2022.03.035. PMID: 35398511.

48. Huang L, Wu T. 2018; Novel neural network application for bacterial colony classification. Theor Biol Med Model. 15:22. DOI: 10.1186/s12976-018-0093-x. PMID: 30373604. PMCID: PMC6206858.

49. Brenton L, Waters MJ, Stanford T, Giglio S. 2020; Clinical evaluation of the APAS® Independence: automated imaging and interpretation of urine cultures using artificial intelligence with composite reference standard discrepant resolution. J Microbiol Methods. 177:106047. DOI: 10.1016/j.mimet.2020.106047. PMID: 32920021.

50. Gammel N, Ross TL, Lewis S, Olson M, Henciak S, Harris R, et al. 2021; Comparison of an automated plate assessment system (APAS Independence) and artificial intelligence (AI) to manual plate reading of methicillin-resistant and methicillin-susceptible Staphylococcus aureus CHROMagar surveillance cultures. J Clin Microbiol. 59:e0097121. DOI: 10.1128/JCM.00971-21. PMID: 34379525. PMCID: PMC8525556.

51. He Y, Peng P, Ying W, Wang Q, Wang Y, Liu X, et al. 2022; Contrast between traditional and machine learning algorithms based on a urine culture predictive model: a multicenter retrospective study in patients with urinary calculi. Transl Androl Urol. 11:139–48. DOI: 10.21037/tau-21-780. PMID: 35280663. PMCID: PMC8899151.

52. Gao Z, Wang L, Zhou L, Zhang J. 2017; HEp-2 cell image classification with deep convolutional neural networks. IEEE J Biomed Health Inform. 21:416–28. DOI: 10.1109/JBHI.2016.2526603. PMID: 26887016.

53. Fang K, Li C, Wang J. 2023; An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning. Brief Bioinform. 24:bbad144. DOI: 10.1093/bib/bbad144. PMID: 37088980.

54. Xiao L, Luo C, Yu T, Luo Y, Wang M, Yu F, et al. 2020; DeepACEv2: automated chromosome enumeration in metaphase cell images using deep convolutional neural networks. IEEE Trans Med Imaging. 39:3920–32. DOI: 10.1109/TMI.2020.3007642. PMID: 32746135.

55. Vajen B, Hänselmann S, Lutterloh F, Käfer S, Espenkötter J, Beening A, et al. 2022; Classification of fluorescent R-band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells. Cancer Genet. 260-261:23–9. DOI: 10.1016/j.cancergen.2021.11.005. PMID: 34839233.

56. Gangadhar A, Sari-Sarraf H, Vanapalli SA. 2023; Deep learning assisted holography microscopy for in-flow enumeration of tumor cells in blood. RSC Adv. 13:4222–35. DOI: 10.1039/D2RA07972K. PMID: 36760296. PMCID: PMC9892890.

57. James G, Witten D, Hastie T, Tibshirani R. 2013. An introduction to statistical learning. Springer;New York: DOI: 10.1007/978-1-4614-7138-7.

58. Cox DR. 1958; The regression analysis of binary sequences. J R Stat Soc B. 20:215–32. DOI: 10.1111/j.2517-6161.1958.tb00292.x.

59. Chung MK. 2020; Introduction to logistic regression. arXiv preprint arXiv:2008.13567. https://doi.org/10.48550/arXiv.2008.13567. DOI: 10.1090/mbk/121/79.

60. Cortes C, Vapnik V. 1995; Support-vector networks. Mach Learn. 20:273–97. DOI: 10.1007/BF00994018.

61. Mahesh B. 2020; Machine learning algorithms-a review. Int J Sci Res. 9:381–6. DOI: 10.21275/ART20203995.

62. Sarker IH. 2021; Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2:160. DOI: 10.1007/s42979-021-00592-x. PMID: 33778771. PMCID: PMC7983091.

63. Goodfellow I, Bengio Y, Courville A. 2016. Deep learning. MIT Press;Cambridge: DOI: 10.1142/9789813146464_0005.

64. Shrestha A, Mahmood A. 2019; Review of deep learning algorithms and architectures. IEEE Access. 7:53040–65. DOI: 10.1109/ACCESS.2019.2912200. PMID: daf0cb47251b44e78bfcaa372d34f37a.

65. Krizhevsky A, Sutskever I, Hinton GE. 2012; Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 25.

66. Krizhevsky A, Sutskever I, Hinton GE. 2017; ImageNet classification with deep convolutional neural networks. Commun ACM. 60:84–90. DOI: 10.1145/3065386.

67. Yamashita R, Nishio M, Do RKG, Togashi K. 2018; Convolutional neural networks: an overview and application in radiology. Insights Imaging. 9:611–29. DOI: 10.1007/s13244-018-0639-9. PMID: 29934920. PMCID: PMC6108980. PMID: dc12a8d883e9458db742fd7c1c320370.

68. Breiman L, Friedman J, editors. 2017. Classification and regression trees. Routledge;Boca Raton: DOI: 10.1201/9781315139470.

69. Breiman L. 2001; Random forests. Mach Learn. 45:5–32. DOI: 10.1023/A:1010933404324.

70. Chen T, Guestrin C. 2016. Xgboost: a scalable tree boosting system. Proc 22nd ACM SIGKDD Intl Conf Knowl Discov Data Min. p. 785–94. DOI: 10.1145/2939672.2939785.

71. Master SR, Badrick TC, Bietenbeck A, Haymond S. 2023; Machine learning in laboratory medicine: recommendations of the IFCC working group. Clin Chem. 69:690–8. DOI: 10.1093/clinchem/hvad055. PMID: 37252943. PMCID: PMC10320011.

72. Rodríguez-Temporal D, Herrera L, Alcaide F, Domingo D, Héry-Arnaud G, van Ingen J, et al. 2023; Identification of Mycobacterium abscessus subspecies by MALDI-TOF mass spectrometry and machine learning. J Clin Microbiol. 61:e0111022. DOI: 10.1128/jcm.01110-22. PMID: 36602341. PMCID: PMC9879094.

73. McFadden BR, Inglis TJJ, Reynolds M. 2023; Machine learning pipeline for blood culture outcome prediction using Sysmex XN-2000 blood sample results in Western Australia. BMC Infect Dis. 23:552. DOI: 10.1186/s12879-023-08535-y. PMID: 37620774. PMCID: PMC10463910. PMID: fd450926dd1a4fc2b7c6cc26a9189ed3.

74. Fang K, Dong Z, Chen X, Zhu J, Zhang B, You J, et al. 2021; Using machine learning to identify clotted specimens in coagulation testing. Clin Chem Lab Med. 59:1289–97. DOI: 10.1515/cclm-2021-0081. PMID: 33660491.

75. Steinbach D, Ahrens PC, Schmidt M, Federbusch M, Heuft L, Lübbert C, et al. 2024; Applying machine learning to blood count data predicts sepsis with ICU admission. Clin Chem. 70:506–15. DOI: 10.1093/clinchem/hvae001. PMID: 38431275.

76. Liao H, Xu Y, Meng Q, Mao Z, Qiao Y, Liu Y, et al. 2023; A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity. Cancer. 129:2986–98. DOI: 10.1002/cncr.34890. PMID: 37254628.

77. Chang YH, Hsiao CT, Chang YC, Lai HY, Lin HH, Chen CC, et al. 2023; Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments. J Microbiol Immunol Infect. 56:782–92. DOI: 10.1016/j.jmii.2023.05.001. PMID: 37244761.

78. Acevedo A, Merino A, Boldú L, Molina Á, Alférez S, Rodellar J. 2021; A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes. Comput Biol Med. 134:104479. DOI: 10.1016/j.compbiomed.2021.104479. PMID: 34010795.

79. Ialongo C, Pieri M, Bernardini S. 2017; Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study. Clin Chem Lab Med. 55:231–6. DOI: 10.1515/cclm-2016-0263. PMID: 27404901.

80. Khan RU, Almakdi S, Alshehri M, Haq AU, Ullah A, Kumar R. 2024; An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images. Heliyon. 10:e26149. DOI: 10.1016/j.heliyon.2024.e26149. PMID: 38384569. PMCID: PMC10879026.

81. Lin YH, Liao KYK, Sung KB. 2020; Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network. J Biomed Opt. 25:116502. DOI: 10.1117/1.JBO.25.11.116502. PMID: 33188571. PMCID: PMC7665881.

82. Sharma S, Gupta S, Gupta D, Juneja S, Gupta P, Dhiman G, et al. 2022; Deep learning model for the automatic classification of white blood cells. Comput Intell Neurosci. 2022:7384131. DOI: 10.1155/2022/7384131. PMID: 35069725. PMCID: PMC8769872.

83. Choi JW, Ku Y, Yoo BW, Kim JA, Lee DS, Chai YJ, et al. 2017; White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PLoS One. 12:e0189259. DOI: 10.1371/journal.pone.0189259. PMID: 29228051. PMCID: PMC5724840. PMID: 0fd267ee1f9848318e1bb87f750a2416.

84. Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, et al. 2023; Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. 183:589–96. DOI: 10.1001/jamainternmed.2023.1838. PMID: 37115527. PMCID: PMC10148230.

85. Kanjee Z, Crowe B, Rodman A. 2023; Accuracy of a generative artificial intelligence model in a complex diagnostic challenge. JAMA. 330:78–80. DOI: 10.1001/jama.2023.8288. PMID: 37318797. PMCID: PMC10273128.

86. Yang HS, Wang F, Greenblatt MB, Huang SX, Zhang Y. 2023; AI chatbots in clinical laboratory medicine: foundations and trends. Clin Chem. 69:1238–46. DOI: 10.1093/clinchem/hvad106. PMID: 37664912.

87. Kurstjens S, Schipper A, Krabbe J, Kusters R. 2024; Predicting hemoglobinopathies using ChatGPT. Clin Chem Lab Med. 62:e59–61. DOI: 10.1515/cclm-2023-0885. PMID: 37650428.

88. Wu AHB, Jaffe AS, Peacock WF, Kavsak P, Greene D, Christenson RH. 2024; The role of artificial intelligence for providing scientific content for laboratory medicine. J Appl Lab Med. 9:386–93. DOI: 10.1093/jalm/jfad095. PMID: 38102068.

89. Cadamuro J, Cabitza F, Debeljak Z, De Bruyne S, Frans G, Perez SM, et al. 2023; Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI). Clin Chem Lab Med. 61:1158–66. DOI: 10.1515/cclm-2023-0355. PMID: 37083166.

90. He H, Garcia EA. 2009; Learning from imbalanced data. IEEE Trans Knowl Data Eng. 21:1263–84. DOI: 10.1109/TKDE.2008.239.

91. Drummond C, Holte RC. 2005. Severe class imbalance: why better algorithms aren't the answer. 3720:In : European Conference on Machine Learning; Springer;Berlin, Heidelberg: p. 539–46. DOI: 10.1007/11564096_52.

92. Carobene A, Milella F, Famiglini L, Cabitza F. 2022; How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data. Clin Chem Lab Med. 60:1887–901. DOI: 10.1515/cclm-2022-0182. PMID: 35508417.

93. Agnello L, Vidali M, Padoan A, Lucis R, Mancini A, Guerranti R, et al. 2024; Machine learning algorithms in sepsis. Clin Chim Acta. 553:117738. DOI: 10.1016/j.cca.2023.117738. PMID: 38158005.

94. Kim S. 2023; Laboratory data quality evaluation in the big data era. Ann Lab Med. 43:399–400. DOI: 10.3343/alm.2023.43.5.399. PMID: 37080739. PMCID: PMC10151286.

95. Cho EJ, Jeong TD, Kim S, Park HD, Yun YM, Chun S, et al. 2023; A new strategy for evaluating the quality of laboratory results for big data research: using external quality assessment survey data (2010-2020). Ann Lab Med. 43:425–33. DOI: 10.3343/alm.2023.43.5.425. PMID: 37080743. PMCID: PMC10151270.

96. Kim S, Cho EJ, Jeong TD, Park HD, Yun YM, Lee K, et al. 2023; Proposed model for evaluating real-world laboratory results for big data research. Ann Lab Med. 43:104–7. DOI: 10.3343/alm.2023.43.1.104. PMID: 36045065. PMCID: PMC9467825.