Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

Clinical Department of Laboratory Medicine University Hospitals Leuven Leuven Belgium

Department of Cardiovascular Sciences KU Leuven Leuven Belgium

Department of Clinical Biochemistry Laboratory for Inherited Metabolic Disorders University Hospital Olomouc and Faculty of Medicine and Dentistry Palacký University in Olomouc Olomouc Czech Republic

Department of Clinical Immunology and Allergy Laboratory of Immunopathology 1 M Sechenov 1st Moscow State Medical University Moscow Russia

Department of Hospital Services and Informatics Philips Research Eindhoven The Netherlands

Department of Laboratory Medicine Hospital Universitario La Paz Madrid Spain

European Federation of Clinical Chemistry and Laboratory Medicine Milan Italy

Institute of Laboratory Medicine member of the German Center for Lung Research Philipps University Marburg Marburg Germany

Ortho Clinical Diagnostics Zaventem Belgium

See more in PubMed

Broughman, JR, Chen, RC. Using big data for quality assessment in oncology. J Comp Eff Res 2016;5:309–19. https://doi.org/10.2217/cer-2015-0021 . DOI

Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: a European strategy for data. COM/2020/66 final; 2020. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020DC0066 .

Kapoor, A. Hands-on artificial intelligence for IoT: expert machine learning and deep learning techniques for developing smarter IoT systems . Birmingham, UK: Packt Publishing Ltd; 2019.

Powers, EM, Shiffman, RN, Melnick, ER, Hickner, A, Sharifi, M. Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review. J Am Med Inf Assoc 2018;25:1556–66. https://doi.org/10.1093/jamia/ocy112 . DOI

Ho, VT, Aikens, RC, Tso, G, Heidenreich, PA, Sharp, C, Asch, SM, et al.. Interruptive electronic alerts for choosing wisely recommendations: a cluster randomized controlled trial. J Am Med Inf Assoc 2022;29:1941–8. https://doi.org/10.1093/jamia/ocac139 . DOI

Tan, SSL, Gao, G, Koch, S. Big data and analytics in healthcare. Methods Inf Med 2015;54:546–7. https://doi.org/10.3414/me15-06-1001 . DOI

SAS . Big data: what it is and why it matters [online]. Available from: https://www.sas.com/en_au/insights/big-data/what-is-big-data.html [Accessed 2 Oct 2022].

Rappaport, SM. Genetic factors are not the major causes of chronic diseases. PLoS One 2016;11:e0154387. https://doi.org/10.1371/journal.pone.0154387 . DOI

Renz, H, Holt, PG, Inouye, M, Logan, AC, Prescott, SL, Sly, PD. An exposome perspective: early-life events and immune development in a changing world. J Allergy Clin Immunol 2017;140:24–40. https://doi.org/10.1016/j.jaci.2017.05.015 . DOI

von Hertzen, L, Beutler, B, Bienenstock, J, Blaser, M, Cani, PD, Eriksson, J, et al.. Helsinki alert of biodiversity and health. Ann Med 2015;47:218–25. https://doi.org/10.3109/07853890.2015.1010226 . DOI

Holgate, ST, Wenzel, S, Postma, DS, Weiss, ST, Renz, H, Sly, PD. Asthma. Nat Rev Dis Prim 2015;1:15025. https://doi.org/10.1038/nrdp.2015.25 . DOI

Gawad, C, Koh, W, Quake, SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet 2016;17:175–88. https://doi.org/10.1038/nrg.2015.16 . DOI

Renz, H, Skevaki, C. Early life microbial exposures and allergy risks: opportunities for prevention. Nat Rev Immunol 2021;21:177–91. https://doi.org/10.1038/s41577-020-00420-y . DOI

Loh, TP, Ranieri, E, Metz, MP. Derivation of pediatric within-individual biological variation by indirect sampling method: an LMS approach. Am J Clin Pathol 2014;142:657–63. https://doi.org/10.1309/ajcphzlqaeyh94hi . DOI

Jones, GRD. Estimates of within-subject biological variation derived from pathology databases: an approach to allow assessment of the effects of age, sex, time between sample collections, and analyte concentration on reference change values. Clin Chem 2019;65:579–88. https://doi.org/10.1373/clinchem.2018.290841 . DOI

Marqués-García, F, Nieto-Librero, A, González-García, N, Galindo-Villardón, P, Martínez-Sánchez, LM, Tejedor-Ganduxé, X, et al.. Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa). Clin Chem Lab Med 2022;60:1804–12. https://doi.org/10.1515/cclm-2021-0863 . DOI

Bunyavanich, S, Schadt, EE. Systems biology of asthma and allergic diseases: a multiscale approach. J Allergy Clin Immunol 2015;135:31–42. https://doi.org/10.1016/j.jaci.2014.10.015 . DOI

Miotto, R, Li, L, Kidd, BA, Dudley, JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 2016;6:26094. https://doi.org/10.1038/srep26094 . DOI

Woodhouse, S, Moignard, V, Göttgens, B, Fisher, J. Processing, visualising and reconstructing network models from single-cell data. Immunol Cell Biol 2016;94:256–65. https://doi.org/10.1038/icb.2015.102 . DOI

Hulsen, T, Jamuar, SS, Moody, AR, Karnes, JH, Varga, O, Hedensted, S, et al.. From big data to precision medicine. Front Med 2019;6:34. https://doi.org/10.3389/fmed.2019.00034 . DOI

McCue, ME, McCoy, AM. The scope of big data in one medicine: unprecedented opportunities and challenges. Front Vet Sci 2017;4:194. https://doi.org/10.3389/fvets.2017.00194 . DOI

Wilkinson, MD, Dumontier, M, Aalbersberg, IJJ, Appleton, G, Axton, M, Baak, A, et al.. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018. https://doi.org/10.1038/sdata.2016.18 . DOI

Hulsen, T. Sharing is caring-data sharing initiatives in healthcare. Int J Environ Res Publ Health 2020;17:3046. https://doi.org/10.3390/ijerph17093046 . DOI

Lamprecht, AL, Garcia, L, Kuzak, M, Martinez, C, Arcila, R, Martin Del Pico, E, et al.. Towards FAIR principles for research software. Data Sci 2020;3:37–59. https://doi.org/10.3233/ds-190026 . DOI

Deist, TM, Dankers, FJWM, Ojha, P, Scott Marshall, M, Janssen, T, Faivre-Finn, C, et al.. Distributed learning on 20 000+ lung cancer patients – the Personal Health Train. Radiother Oncol 2020;144:189–200. https://doi.org/10.1016/j.radonc.2019.11.019 . DOI

Health, RI. Personal health train [online]. Available from: https://www.health-ri.nl/initiatives/personal-health-train [Accessed 21 Oct 2022].

Bender, D, Sartipi, K. HL7 FHIR: an Agile and RESTful approach to healthcare information exchange. In: Proceedings of the 26th IEEE international symposium on computer-based medical systems . Porto, Portugal: IEEE; 2013.

Donnelly, K. SNOMED-CT: the advanced terminology and coding system for eHealth. Stud Health Technol Inf 2006;121:279–90.

Forrey, AW, McDonald, CJ, DeMoor, G, Huff, SM, Leavelle, D, Leland, D, et al.. Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. Clin Chem 1996;42:81–90. https://doi.org/10.1093/clinchem/42.1.81 . DOI

The Gene Ontology Consortium . The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res 2019;47:D330–8. https://doi.org/10.1093/nar/gky1055 . DOI

Gunning, D, Stefik, M, Choi, J, Miller, T, Stumpf, S, Yang, GZ. XAI-Explainable artificial intelligence. Sci Robot 2019;4:eaay7120. https://doi.org/10.1126/scirobotics.aay7120 . DOI

Ghassemi, M, Oakden-Rayner, L, Beam, AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 2021;3:e745–50. https://doi.org/10.1016/s2589-7500(21)00208-9 . DOI

Holzinger, A, Langs, G, Denk, H, Zatloukal, K, Müller, H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov 2019;9:e1312. https://doi.org/10.1002/widm.1312 . DOI

Hulsen, T. Challenges and solutions for big data in personalized healthcare. In: Moustafa, AA, editor. Big data in psychiatry & neurology . Amsterdam, The Netherlands: Elsevier; 2021.

Asan, O, Bayrak, AE, Choudhury, A. Artificial intelligence and human trust in healthcare: focus on clinicians. J Med Internet Res 2020;22:e15154. https://doi.org/10.2196/15154 . DOI

Altman, EI. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 1968;23:589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x . DOI

Robertson, EA, Zweig, MH. Use of receiver operating characteristic curves to evaluate the clinical performance of analytical systems. Clin Chem 1981;27:1569–74. https://doi.org/10.1093/clinchem/27.9.1569 . DOI

Cox, DR. Regression models and life-tables. J R Stat Soc Series B Stat Methodol 1972;34:187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x . DOI

Wolrab, D, Jirásko, R, Cífková, E, Höring, M, Mei, D, Chocholoušková, M, et al.. Lipidomic profiling of human serum enables detection of pancreatic cancer. Nat Commun 2022;13:124. https://doi.org/10.1038/s41467-021-27765-9 . DOI

Mayo Clinic. CERAM : MI-heart ceramides, plasma [online]. Available from: https://www.mayocliniclabs.com/test-catalog/Overview/606777 [Accessed 2 Oct 2022].

Mayo, Clinic. CLIR – Collaborative Laboratory Integrated Reports [online]. Available from: https://clir.mayo.edu/ [Accessed 2 Oct 2022].

Aris-Brosou, S, Kim, J, Li, L, Liu, H. Predicting the reasons of customer complaints: a first step toward anticipating quality issues of in Vitro diagnostics assays with machine learning. JMIR Med Inform 2018;6:e34. https://doi.org/10.2196/medinform.9960 . DOI

Badrick, T, Graham, P. Can a combination of average of normals and “real time” external quality assurance replace internal quality control? Clin Chem Lab Med 2018;56:549–53. https://doi.org/10.1515/cclm-2017-0115 . DOI