Journal Home > Volume 1 , Issue 1
iLABMED 2023, 1(1): 82-91 https://doi.org/10.1002/ila2.9
Review | Open Access | Issue | Published: 09 May 2023

Clinical application of intelligent technologies and integration in medical laboratories

Show Author's Information Wenjie Huang1,,  Dongquan Huang1,,  Yeran Ding1 ,,  Cheng Yu2,  Lei Wang2,  Ning Lv1( ),  Jiuxin Qu1( ),  Hongzhou Lu3( )
Department of Clinical Laboratory, National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Infectious Diseases (Tuberculosis), Shenzhen Clinical Research Center for Tuberculosis, The Third People's Hospital of Shenzhen, Southern University of Science and Technology, Shenzhen, Guangdong, China
Department of Centralized and Point of Care Solutions & Molecular Diagnostics, Roche Diagnostics (Shanghai) Ltd, Shanghai, China
National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen and the Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, Guangdong, China

Wenjie Huang, Dongquan Huang, Yeran Ding represents the co‐first author.

Keywords:
integration, intelligence, AI/ML, medical laboratory
Cite this article:
Huang W, Huang D, Ding Y, et al. Clinical application of intelligent technologies and integration in medical laboratories. iLABMED, 2023, 1(1): 82-91. https://doi.org/10.1002/ila2.9
Download citation
EndNote(RIS)
BibTeX

198

Views

9

Downloads

Citations

2

Crossref

N/A

WoS

N/A

Scopus

N/A

CSCD

Abstract Full text About this article

With the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.


menu
Abstract
Full text
Outline
About this article

Clinical application of intelligent technologies and integration in medical laboratories

Show Author's information Wenjie Huang1,, Dongquan Huang1,, Yeran Ding1 ,, Cheng Yu2, Lei Wang2, Ning Lv1( ), Jiuxin Qu1( ), Hongzhou Lu3( )
Department of Clinical Laboratory, National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Infectious Diseases (Tuberculosis), Shenzhen Clinical Research Center for Tuberculosis, The Third People's Hospital of Shenzhen, Southern University of Science and Technology, Shenzhen, Guangdong, China
Department of Centralized and Point of Care Solutions & Molecular Diagnostics, Roche Diagnostics (Shanghai) Ltd, Shanghai, China
National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen and the Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, Guangdong, China

Wenjie Huang, Dongquan Huang, Yeran Ding represents the co‐first author.

Abstract

With the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.

Keywords: integration, intelligence, AI/ML, medical laboratory

References(51)

[1]
ThairSMewesCHinzJBergmannIBüttnerBSehmischSGene expression‐based diagnosis of infections in critically Ill patients‐prospective validation of the SepsisMetaScore in a longitudinal severe trauma cohortCrit Care Med2021498e751e76010.1097/ccm.0000000000005027

Thair S, Mewes C, Hinz J, Bergmann I, Büttner B, Sehmisch S, et al. Gene expression‐based diagnosis of infections in critically Ill patients‐prospective validation of the SepsisMetaScore in a longitudinal severe trauma cohort. Crit Care Med. 2021;49(8):e751–e760. https://doi.org/10.1097/ccm.0000000000005027
DOI Google Scholar
[2]
TsalikELHenaoRMontgomeryJLNawrockiJWAydinMLydonECDiscriminating bacterial and viral infection using a rapid host gene expression testCrit Care Med2021491016516310.1097/CCM.0000000000005085

Tsalik EL, Henao R, Montgomery JL, Nawrocki JW, Aydin M, Lydon EC, et al. Discriminating bacterial and viral infection using a rapid host gene expression test. Crit Care Med. 2021;49(10):1651–63. https://doi.org/10.1097/ccm.0000000000005085
DOI Google Scholar
[3]

Shortliffe EH. Computer‐based medical consultations: mycin. Elsevier; 2012. 9780444569691.
[4]

Wilson S, Steele S, Adeli K. Innovative technological advancements in laboratory medicine: predicting the lab of the future. Biotechnol Biotechnol Equip. 2022;36(Suppl 1):S9–S21. https://doi.org/10.1080/13102818.2021.2011413
DOI Google Scholar
[5]

Plebani M. Errors in clinical laboratories or errors in laboratory medicine? Clin Chem Lab Med. 2006;44(6):750–9. https://doi.org/10.1515/cclm.2006.123
DOI Google Scholar
[6]

Pendleton B, LaFaye R. Multicenter study of needle‐free blood collection system for reducing specimen error and intravenous catheter replacement. J Healthc Qual. 2022;44(2):e24–30. https://doi.org/10.1097/jhq.0000000000000331
DOI Google Scholar
[7]
LeipheimerJMBalterMLChenAIPantinEJDavidovichAELabazzoKSFirst‐in‐human evaluation of a hand‐held automated venipuncture device for rapid venous blood drawsTechnology (Singap World Sci)201973‐49810710.1142/s2339547819500067

Leipheimer JM, Balter ML, Chen AI, Pantin EJ, Davidovich AE, Labazzo KS, et al. First‐in‐human evaluation of a hand‐held automated venipuncture device for rapid venous blood draws. Technology (Singap World Sci). 2019;7(3‐4):98–107. https://doi.org/10.1142/s2339547819500067
DOI Google Scholar
[8]
SuchslandJWinterTGreiserAStreichertTOttoBMayerleJExtending laboratory automation to the wards: effect of an innovative pneumatic tube system on diagnostic samples and transport timeClin Chem Lab Med20175522253010.1515/cclm-2016-0380

Suchsland J, Winter T, Greiser A, Streichert T, Otto B, Mayerle J, et al. Extending laboratory automation to the wards: effect of an innovative pneumatic tube system on diagnostic samples and transport time. Clin Chem Lab Med. 2017;55(2):225–30. https://doi.org/10.1515/cclm-2016-0380
DOI Google Scholar
[9]

Brown AS, Badrick T. The next wave of innovation in laboratory automation: systems for auto‐verification, quality control and specimen quality assurance. Clin Chem Lab Med. 2023;61(1):37–43. https://doi.org/10.1515/cclm-2022-0409
DOI Google Scholar
[10]

Naugler C, Church DL. Automation and artificial intelligence in the clinical laboratory. Crit Rev Clin Lab Sci. 2019;56(2):98–110. https://doi.org/10.1080/10408363.2018.1561640
DOI Google Scholar
[11]

Bailey AL, Ledeboer N, Burnham CD. Clinical microbiology is growing up: the total laboratory automation revolution. Clin Chem. 2019;65(5):634–43. https://doi.org/10.1373/clinchem.2017.274522
DOI Google Scholar
[12]
YangCLiDSunDZhangSZhangPXiongYA deep learning‐based system for assessment of serum quality using sample imagesClin Chim Acta20225312546010.1016/j.cca.2022.04.010

Yang C, Li D, Sun D, Zhang S, Zhang P, Xiong Y, et al. A deep learning‐based system for assessment of serum quality using sample images. Clin Chim Acta. 2022;531:254–60. https://doi.org/10.1016/j.cca.2022.04.010
DOI Google Scholar
[13]

Lippi G, Da Rin G. Advantages and limitations of total laboratory automation: a personal overview. Clin Chem Lab Med. 2019;57(6):802–11. https://doi.org/10.1515/cclm-2018-1323
DOI Google Scholar
[14]
HuangXJiangYLiuXXuHHanZRongHMachine learning based single‐frame super‐resolution processing for lensless blood cell countingSensors (Basel)20161611183610.3390/s16111836

Huang X, Jiang Y, Liu X, Xu H, Han Z, Rong H, et al. Machine learning based single‐frame super‐resolution processing for lensless blood cell counting. Sensors (Basel). 2016;16(11):1836. https://doi.org/10.3390/s16111836
DOI Google Scholar
[15]

Abid F, Hamami L. A survey of neural network based automated systems for human chromosome classification. Artif Intell Rev. 2018;49(1):41–56. https://doi.org/10.1007/s10462-016-9515-5
DOI Google Scholar
[16]
LingJWangHLiGFengZSongYWangPA novel short‐term high‐lactose culture approach combined with a matrix‐assisted laser desorption ionization‐time of flight mass spectrometry assay for differentiating Escherichia coli and Shigella species using artificial neural networksPLoS One20191410e022263610.1371/journal.pone.0222636

Ling J, Wang H, Li G, Feng Z, Song Y, Wang P, et al. A novel short‐term high‐lactose culture approach combined with a matrix‐assisted laser desorption ionization‐time of flight mass spectrometry assay for differentiating Escherichia coli and Shigella species using artificial neural networks. PLoS One. 2019;14(10):e0222636. https://doi.org/10.1371/journal.pone.0222636
DOI Google Scholar
[17]
TranNKHowardTWalshRPepperJLoegeringJPhinneyBNovel application of automated machine learning with MALDI‐TOF‐MS for rapid high‐throughput screening of COVID‐19: a proof of conceptSci Rep2021111821910.1038/s41598-021-87463-w

Tran NK, Howard T, Walsh R, Pepper J, Loegering J, Phinney B, et al. Novel application of automated machine learning with MALDI‐TOF‐MS for rapid high‐throughput screening of COVID‐19: a proof of concept. Sci Rep. 2021;11(1):8219. https://doi.org/10.1038/s41598-021-87463-w
DOI Google Scholar
[18]

Weis CV, Jutzeler CR, Borgwardt K. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI‐TOF mass spectra: a systematic review. Clin Microbiol Infect. 2020;26(10):1310–7. https://doi.org/10.1016/j.cmi.2020.03.014
DOI Google Scholar
[19]

Lindberg DA. Collection, evaluation, and transmission of hospital laboratory data. Methods Inf Med. 1967;6(3):97–107. https://doi.org/10.1055/s-0038-1636364
DOI Google Scholar
[20]
YanCZhangYLiJGaoJCuiCZhangCEstablishing and validating of an laboratory information system‐based auto‐verification system for biochemical test results in cancer patientsJ Clin Lab Anal2019335e2287710.1002/jcla.22877

Yan C, Zhang Y, Li J, Gao J, Cui C, Zhang C, et al. Establishing and validating of an laboratory information system‐based auto‐verification system for biochemical test results in cancer patients. J Clin Lab Anal. 2019;33(5):e22877. https://doi.org/10.1002/jcla.22877
DOI Google Scholar
[21]

Wang H, Wang H, Zhang J, Li X, Sun C, Zhang Y. Using machine learning to develop an autoverification system in a clinical biochemistry laboratory. Clin Chem Lab Med. 2021;59(5):883–91. https://doi.org/10.1515/cclm-2020-0716
DOI Google Scholar
[22]

McRae MP, Rajsri KS, Alcorn TM, McDevitt JT. Smart diagnostics: combining artificial intelligence and in vitro diagnostics. Sensors (Basel). 2022;22(17):6355. https://doi.org/10.3390/s22176355
DOI Google Scholar
[23]
YanWShiHHeTChenJWangCLiaoAEmployment of artificial intelligence based on routine laboratory results for the early diagnosis of multiple myelomaFront Oncol20211160819110.3389/fonc.2021.608191

Yan W, Shi H, He T, Chen J, Wang C, Liao A, et al. Employment of artificial intelligence based on routine laboratory results for the early diagnosis of multiple myeloma. Front Oncol. 2021;11:608191. https://doi.org/10.3389/fonc.2021.608191
DOI Google Scholar
[24]
HuangBHuangHZhangSZhangDShiQLiuJArtificial intelligence in pancreatic cancerTheranostics2022121669315410.7150/thno.77949

Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, et al. Artificial intelligence in pancreatic cancer. Theranostics. 2022;12(16):6931–54. https://doi.org/10.7150/thno.77949
DOI Google Scholar
[25]

Azarkhish I, Raoufy MR, Gharibzadeh S. Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data. J Med Syst. 2012;36(3):2057–61. https://doi.org/10.1007/s10916-011-9668-3
DOI Google Scholar
[26]

American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(Suppl 1):S67–74. https://doi.org/10.2337/dc13-S067
DOI Google Scholar
[27]

Oosterhuis W. Adding clinical utility to the laboratory reports: automation of interpretative comments. Clin Chem Lab Med. 2019;57(3):365–70. https://doi.org/10.1515/cclm-2018-0623
DOI Google Scholar
[28]

Cardozo G, Tirloni SF, Pereira Moro AR, Marques JLB. Use of artificial intelligence in the search for new information through routine laboratory tests: systematic review. JMIR Bioinform Biotech. 2022;3(1):e40473. https://doi.org/10.2196/40473
DOI Google Scholar
[29]
TranNKAlbahraSMayLWaldmanSCrabtreeSBainbridgeSEvolving applications of artificial intelligence and machine learning in infectious diseases testingClin Chem20216811253310.1093/clinchem/hvab239

Tran NK, Albahra S, May L, Waldman S, Crabtree S, Bainbridge S, et al. Evolving applications of artificial intelligence and machine learning in infectious diseases testing. Clin Chem. 2021;68(1):125–33. https://doi.org/10.1093/clinchem/hvab239
DOI Google Scholar
[30]
WübboldingMLopez AlfonsoJCLinCYBinderSFalkCDebarryJPilot study using machine learning to identify immune profiles for the prediction of early virological relapse after stopping nucleos(t)ide analogues in HBeAg‐negative CHBHepatol Commun2021519711110.1002/hep4.1626

Wübbolding M, Lopez Alfonso JC, Lin CY, Binder S, Falk C, Debarry J, et al. Pilot study using machine learning to identify immune profiles for the prediction of early virological relapse after stopping nucleos(t)ide analogues in HBeAg‐negative CHB. Hepatol Commun. 2021;5(1):97–111. https://doi.org/10.1002/hep4.1626
DOI Google Scholar
[31]

Islam MM, Yang HC, Poly TN, Li YJ. Development of an artificial intelligence‐based automated recommendation system for clinical laboratory tests: retrospective analysis of the national health insurance database. JMIR Med Inform. 2020;8(11):e24163. https://doi.org/10.2196/24163
DOI Google Scholar
[32]

Islam MM, Poly TN, Yang HC, Li YJ. Deep into laboratory: an artificial intelligence approach to recommend laboratory tests. Diagnostics (Basel). 2021;11(6):990. https://doi.org/10.3390/diagnostics11060990
DOI Google Scholar
[33]
XuSHomJBalasubramanianSSchroederLFNajafiNRoySPrevalence and predictability of low‐yield inpatient laboratory diagnostic testsJAMA Netw Open201929e191096710.1001/jamanetworkopen.2019.10967

Xu S, Hom J, Balasubramanian S, Schroeder LF, Najafi N, Roy S, et al. Prevalence and predictability of low‐yield inpatient laboratory diagnostic tests. JAMA Netw Open. 2019;2(9):e1910967. https://doi.org/10.1001/jamanetworkopen.2019.10967
DOI Google Scholar
[34]

Yu L, Zhang Q, Bernstam EV, Jiang X. Predict or draw blood: an integrated method to reduce lab tests. J Biomed Inform. 2020;104:103394. https://doi.org/10.1016/j.jbi.2020.103394
DOI Google Scholar
[35]
WatsonIDOosterhuisWPJorgensenPEDikmenZGSiodmiakJJovicicSA survey of patients' views from eight European countries of interpretive support from Specialists in laboratory medicineClin Chem Lab Med20175510149650010.1515/cclm-2017-0080

Watson ID, Oosterhuis WP, Jorgensen PE, Dikmen ZG, Siodmiak J, Jovicic S, et al. A survey of patients' views from eight European countries of interpretive support from Specialists in laboratory medicine. Clin Chem Lab Med. 2017;55(10):1496–500. https://doi.org/10.1515/cclm-2017-0080
DOI Google Scholar
[36]
JovičićSSiodmiakJAlcortaMDKittelMOosterhuisWAakreKMQuality benchmarking of smartphone laboratory medicine applications: comparison of laboratory medicine specialists' and non‐laboratory medicine professionals' evaluationClin Chem Lab Med2021594693910.1515/cclm-2020-0869

Jovičić S, Siodmiak J, Alcorta MD, Kittel M, Oosterhuis W, Aakre KM, et al. Quality benchmarking of smartphone laboratory medicine applications: comparison of laboratory medicine specialists' and non‐laboratory medicine professionals' evaluation. Clin Chem Lab Med. 2021;59(4):693–9. https://doi.org/10.1515/cclm-2020-0869
DOI Google Scholar
[37]
GrusonDDablaPStankovicSHomsakEGougetBBernardiniSArtificial intelligence and thyroid disease management: considerations for thyroid function testsBiochem Med (Zagreb)202232202060110.11613/bm.2022.020601

Gruson D, Dabla P, Stankovic S, Homsak E, Gouget B, Bernardini S, et al. Artificial intelligence and thyroid disease management: considerations for thyroid function tests. Biochem Med (Zagreb). 2022;32(2):020601. https://doi.org/10.11613/bm.2022.020601
DOI Google Scholar
[38]

Ngiam KY, Khor IW. Big data and machine learning algorithms for health‐care delivery. Lancet Oncol. 2019;20(5):e262–e73. https://doi.org/10.1016/s1470-2045(19)30149-4
DOI Google Scholar
[39]

Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi‐functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020:baaa010. https://doi.org/10.1093/database/baaa010
DOI Google Scholar
[40]

Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digital Med. 2020;3:30. https://doi.org/10.1038/s41746-020-0229-3
DOI Google Scholar
[41]
Ambale‐VenkateshBYangXWuCOLiuKHundleyWGMcClellandRCardiovascular event prediction by machine learning: the multi‐ethnic study of atherosclerosisCirc Res20171219109210110.1161/CIRCRESAHA.117.311312

Ambale‐Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction by machine learning: the multi‐ethnic study of atherosclerosis. Circ Res. 2017;121(9):1092–101. https://doi.org/10.1161/circresaha.117.311312
DOI Google Scholar
[42]

Lobo B, Abdel‐Rahman E, Brown D, Dunn L, Bowman B. A recurrent neural network approach to predicting hemoglobin trajectories in patients with end‐stage renal disease. Artif Intell Med. 2020;104:101823. https://doi.org/10.1016/j.artmed.2020.101823
DOI Google Scholar
[43]
Bi‐directional gated recurrent unit based ensemble model for the early detection of sepsis. In: Mahmud MS, editors. 2020 42nd annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2020.
[44]
Whitehouse M, Rojanasakul MJB. Find out if your job will Be automated; 2017.
[45]

Hussain OA, Junejo KN. Predicting treatment outcome of drug‐susceptible tuberculosis patients using machine‐learning models. Inform Health Soc Care. 2019;44(2):135–51. https://doi.org/10.1080/17538157.2018.1433676
DOI Google Scholar
[46]
DongJWuHZhouDLiKZhangYJiHApplication of big data and artificial intelligence in COVID‐19 prevention, diagnosis, treatment and management decisions in ChinaJ Med Syst20214598410.1007/s10916-021-01757-0

Dong J, Wu H, Zhou D, Li K, Zhang Y, Ji H, et al. Application of big data and artificial intelligence in COVID‐19 prevention, diagnosis, treatment and management decisions in China. J Med Syst. 2021;45(9):84. https://doi.org/10.1007/s10916-021-01757-0
DOI Google Scholar
[47]
SomnayYRCravenMMcCoyKLCartySEWangTSGreenbergCCImproving diagnostic recognition of primary hyperparathyroidism with machine learningSurgery2017161411132110.1016/j.surg.2016.09.044

Somnay YR, Craven M, McCoy KL, Carty SE, Wang TS, Greenberg CC, et al. Improving diagnostic recognition of primary hyperparathyroidism with machine learning. Surgery. 2017;161(4):1113–21. https://doi.org/10.1016/j.surg.2016.09.044
DOI Google Scholar
[48]
CarobeneACabitzaFBernardiniSGopalanRLennerzJKWeirCWhere is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratoriesClin Chem Lab Med20236145354310.1515/cclm-2022-1030

Carobene A, Cabitza F, Bernardini S, Gopalan R, Lennerz JK, Weir C, et al. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories. Clin Chem Lab Med. 2023;61(4):535–43. https://doi.org/10.1515/cclm-2022-1030
DOI Google Scholar
[49]

Neumaier M. Diagnostics 4.0: the medical laboratory in digital health. Clin Chem Lab Med. 2019;57(3):343–8. https://doi.org/10.1515/cclm-2018-1088
DOI Google Scholar
[50]

Bai J, Shen L, Sun H, Shen B. Physiological informatics: collection and analyses of data from wearable sensors and smartphone for healthcare. Adv Exp Med Biol. 2017;1028:17–37. https://doi.org/10.1007/978-981-10-6041-0_2
DOI Google Scholar
[51]

Pennestrì F, Banfi G. Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key‐points. Clin Chem Lab Med. 2022;60(12):1867–74. https://doi.org/10.1515/cclm-2022-0096
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 27 February 2023
Accepted: 13 March 2023
Published: 09 May 2023
Issue date: June 2023

Copyright

© 2023 The Authors. Tsinghua University Press.

Acknowledgements

ACKNOWLEDGMENTS

None.

Rights and permissions

This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Return