Monti C, Zilocchi M, Colugnat I, Alberio T. Proteomics turns functional. J Proteom. 2019;198:36–44.
Article
CAS
Google Scholar
Prodan Žitnik I, Černe D, Mancini I, Simi L, Pazzagli M, Di Resta C, et al. Personalized laboratory medicine: a patient-centered future approach. Clin Chem Lab Med. 2018;56:1981–91.
Article
PubMed
Google Scholar
Pareek CS, Smoczynski R, Tretyn A. Sequencing technologies and genome sequencing. J Appl Genet. 2011;52:413–35.
Article
CAS
PubMed
PubMed Central
Google Scholar
Olfson E, Cottrell CE, Davidson NO, Gurnett CA, Heusel JW, Stitziel NO, et al. Identification of medically actionable secondary findings in the 1000 genomes. PLoS ONE. 2015;10:e0135193.
Article
PubMed
PubMed Central
Google Scholar
Harel T, Lupski JR. Genomic disorders 20 years on-mechanisms for clinical manifestations. Clin Genet. 2018;93:439–49.
Article
CAS
PubMed
Google Scholar
Cifani P, Kentsis A. Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease. Proteomics. 2017;17:155.
Article
Google Scholar
Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52:1122–31.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30:1234–48.
Article
PubMed
PubMed Central
Google Scholar
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.
Article
PubMed
PubMed Central
Google Scholar
Bayot ML, Brannan GD, Naidoo P. Clinical laboratory. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 [cited 2022 Jan 20]. http://www.ncbi.nlm.nih.gov/books/NBK535358/.
Park JY, Kricka LJ. One hundred years of clinical laboratory automation: 1967–2067. Clin Biochem. 2017;50:639–44.
Article
PubMed
Google Scholar
Bailey AL, Ledeboer N, Burnham C-AD. Clinical microbiology is growing up: the total laboratory automation revolution. Clin Chem. 2019;65:634–43.
Article
CAS
PubMed
Google Scholar
Naugler C, Church DL. Automation and artificial intelligence in the clinical laboratory. Crit Rev Clin Lab Sci. 2019;56:98–110.
Article
PubMed
Google Scholar
Nakamine Y. Reflections on the activities of the past year. Public health nursing activities and evaluation. Hokenfu Zasshi. 1987;43:1061.
CAS
PubMed
Google Scholar
Thomson RB, McElvania E. Total laboratory automation: what is gained, what is lost, and who can afford it? Clin Lab Med. 2019;39:371–89.
Article
PubMed
Google Scholar
Ma C, Wang X, Wu J, Cheng X, Xia L, Xue F, et al. Real-world big-data studies in laboratory medicine: current status, application, and future considerations. Clin Biochem. 2020;84:21–30.
Article
CAS
PubMed
Google Scholar
Vesper HW, Myers GL, Miller WG. Current practices and challenges in the standardization and harmonization of clinical laboratory tests. Am J Clin Nutr. 2016;104(Suppl 3):907S-S912.
Article
CAS
PubMed
PubMed Central
Google Scholar
Thelen MHM, Vanstapel FJLA, Kroupis C, Vukasovic I, Boursier G, Barrett E, et al. Flexible scope for ISO 15189 accreditation: a guidance prepared by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group Accreditation and ISO/CEN standards (WG-A/ISO). Clin Chem Lab Med. 2015;53:1173–80.
Article
CAS
PubMed
Google Scholar
Huisman W. European medical laboratory accreditation. Present situation and steps to harmonisation. Clin Chem Lab Med. 2012;50:1147–52.
Article
CAS
PubMed
Google Scholar
Schreier J, Feeney R, Keeling P. Diagnostics reform and harmonization of clinical laboratory testing. J Mol Diagn. 2019;21:737–45.
Article
PubMed
Google Scholar
Koupenova M, Clancy L, Corkrey HA, Freedman JE. Circulating platelets as mediators of immunity, inflammation, and thrombosis. Circ Res. 2018;122:337–51.
Article
CAS
PubMed
PubMed Central
Google Scholar
Holinstat M. Normal platelet function. Cancer Metastasis Rev. 2017;36:195–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
In’t Veld SGJG, Wurdinger T. Tumor-educated platelets. Blood. 2019;133:2359–64.
Article
Google Scholar
Zu R, Yu S, Yang G, Ge Y, Wang D, Zhang L, et al. Integration of platelet features in blood and platelet rich plasma for detection of lung cancer. Clin Chim Acta. 2020;509:43–51.
Article
CAS
PubMed
Google Scholar
Best MG, Wesseling P, Wurdinger T. Tumor-educated platelets as a noninvasive biomarker source for cancer detection and progression monitoring. Cancer Res. 2018;78:3407–12.
Article
CAS
PubMed
Google Scholar
Smith SH. Using albumin and prealbumin to assess nutritional status. Nursing. 2017;47:65–6.
Article
PubMed
Google Scholar
Kawai H, Ota H. Low perioperative serum prealbumin predicts early recurrence after curative pulmonary resection for non-small-cell lung cancer. World J Surg. 2012;36:2853–7.
Article
PubMed
Google Scholar
Wei J, Jin M, Shao Y, Ning Z, Huang J. High preoperative serum prealbumin predicts long-term survival in resected esophageal squamous cell cancer. Cancer Manag Res. 2019;11:7997–8003.
Article
CAS
PubMed
PubMed Central
Google Scholar
Qiao W, Leng F, Liu T, Wang X, Wang Y, Chen D, et al. Prognostic value of prealbumin in liver cancer: a systematic review and meta-analysis. Nutr Cancer. 2020;72:909–16.
Article
CAS
PubMed
Google Scholar
Zu H, Wang H, Li C, Xue Y. Preoperative prealbumin levels on admission as an independent predictive factor in patients with gastric cancer. Medicine (Baltimore). 2020;99:e19196.
Article
CAS
Google Scholar
Tomo S, Karli S, Dharmalingam K, Yadav D, Sharma P. The clinical laboratory: a key player in diagnosis and management of COVID-19. EJIFCC. 2020;31:326–46.
CAS
PubMed
PubMed Central
Google Scholar
Chen Z, Xu W, Ma W, Shi X, Li S, Hao M, et al. Clinical laboratory evaluation of COVID-19. Clin Chim Acta. 2021;519:172–82.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hong KH, Lee SW, Kim TS, Huh HJ, Lee J, Kim SY, et al. Guidelines for laboratory diagnosis of coronavirus disease 2019 (COVID-19) in Korea. Ann Lab Med. 2020;40:351–60.
Article
CAS
PubMed
PubMed Central
Google Scholar
Henry BM, de Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med. 2020;58:1021–8.
Article
CAS
PubMed
Google Scholar
Goudouris ES. Laboratory diagnosis of COVID-19. J Pediatr (Rio J). 2021;97:7–12.
Article
Google Scholar
Zhou J, He Z, Ma S, Liu R. AST/ALT ratio as a significant predictor of the incidence risk of prostate cancer. Cancer Med. 2020;9:5672–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Knittelfelder O, Delago D, Jakse G, Reinisch S, Partl R, Stranzl-Lawatsch H, et al. The AST/ALT (De Ritis) ratio predicts survival in patients with oral and oropharyngeal cancer. Diagnostics (Basel). 2020;10:E973.
Article
Google Scholar
Bezan A, Mrsic E, Krieger D, Stojakovic T, Pummer K, Zigeuner R, et al. The preoperative AST/ALT (De Ritis) ratio represents a poor prognostic factor in a cohort of patients with nonmetastatic renal cell carcinoma. J Urol. 2015;194:30–5.
Article
PubMed
Google Scholar
Hu X, Yang W-X, Wang Y, Shao Y-X, Xiong S-C, Li X. The prognostic value of De Ritis (AST/ALT) ratio in patients after surgery for urothelial carcinoma: a systematic review and meta-analysis. Cancer Cell Int. 2020;20:39.
Article
PubMed
PubMed Central
Google Scholar
Ishihara H, Kondo T, Yoshida K, Omae K, Takagi T, Iizuka J, et al. Evaluation of preoperative aspartate transaminase/alanine transaminase ratio as an independent predictive biomarker in patients with metastatic renal cell carcinoma undergoing cytoreductive nephrectomy: a propensity score matching study. Clin Genitourin Cancer. 2017;15:598–604.
Article
PubMed
Google Scholar
Sahin AG, Aydin C, Unver M, Pehlivanoglu K. Predictive value of preoperative neutrophil lymphocyte ratio in determining the stage of gastric tumor. Med Sci Monit. 2017;23:1973–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Haram A, Boland MR, Kelly ME, Bolger JC, Waldron RM, Kerin MJ. The prognostic value of neutrophil-to-lymphocyte ratio in colorectal cancer: a systematic review. J Surg Oncol. 2017;115:470–9.
Article
CAS
PubMed
Google Scholar
Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. 2017;111:176–81.
Article
PubMed
Google Scholar
Russo A, Russano M, Franchina T, Migliorino MR, Aprile G, Mansueto G, et al. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and outcomes with nivolumab in pretreated non-small cell lung cancer (NSCLC): a large retrospective multicenter study. Adv Ther. 2020;37:1145–55.
Article
CAS
PubMed
Google Scholar
Sakai M, Sohda M, Saito H, Ubukata Y, Nakazawa N, Kuriyama K, et al. Comparative analysis of immunoinflammatory and nutritional measures in surgically resected esophageal cancer: a single-center retrospective study. In Vivo. 2020;34:881–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Rossi S, Basso M, Strippoli A, Schinzari G, D’Argento E, Larocca M, et al. Are markers of systemic inflammation good prognostic indicators in colorectal cancer? Clin Colorectal Cancer. 2017;16:264–74.
Article
PubMed
Google Scholar
Takagi K, Yagi T, Umeda Y, Shinoura S, Yoshida R, Nobuoka D, et al. Preoperative controlling nutritional status (CONUT) score for assessment of prognosis following hepatectomy for hepatocellular carcinoma. World J Surg. 2017;41:2353–60.
Article
PubMed
Google Scholar
Kuroda D, Sawayama H, Kurashige J, Iwatsuki M, Eto T, Tokunaga R, et al. Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer. 2018;21:204–12.
Article
PubMed
Google Scholar
Sun X, Luo L, Zhao X, Ye P. Controlling Nutritional Status (CONUT) score as a predictor of all-cause mortality in elderly hypertensive patients: a prospective follow-up study. BMJ Open. 2017;7:e015649.
Article
PubMed
PubMed Central
Google Scholar
Holmes JH, Sacchi L, Bellazzi R, Peek N. Artificial intelligence in medicine AIME 2015. Artif Intell Med. 2017;81:1–2.
Article
PubMed
Google Scholar
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500–10.
Article
CAS
PubMed
PubMed Central
Google Scholar
Salto-Tellez M, Maxwell P, Hamilton P. Artificial intelligence-the third revolution in pathology. Histopathology. 2019;74:372–6.
Article
PubMed
Google Scholar
Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103:167–75.
Article
PubMed
Google Scholar
Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71:2668–79.
Article
PubMed
Google Scholar
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268:70–6.
Article
PubMed
Google Scholar
Lippi G. Machine learning in laboratory diagnostics: valuable resources or a big hoax? Diagnosis (Berl). 2019;8:133–5.
Article
Google Scholar
De Bruyne S, Speeckaert MM, Van Biesen W, Delanghe JR. Recent evolutions of machine learning applications in clinical laboratory medicine. Crit Rev Clin Lab Sci. 2021;58:131–52.
Article
PubMed
Google Scholar
Deo RC. Machine learning in medicine. Circulation. 2015;132:1920–30.
Article
PubMed
PubMed Central
Google Scholar
Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer. Behav Ther. 2020;51:675–87.
Article
PubMed
PubMed Central
Google Scholar
Ialongo C, Bernardini S. Total laboratory automation has the potential to be the field of application of artificial intelligence: the cyber-physical system and “Automation 4.0.” Clin Chem Lab Med. 2019;57:e279–81.
Article
CAS
PubMed
Google Scholar
Cabitza F, Banfi G. Machine learning in laboratory medicine: waiting for the flood? Clin Chem Lab Med. 2018;56:516–24.
Article
CAS
PubMed
Google Scholar
Rabbani N, Kim GYE, Suarez CJ, Chen JH. Applications of machine learning in routine laboratory medicine: Current state and future directions. Clin Biochem. 2022;103:1–7.
Article
PubMed
Google Scholar
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. 2021;11:372.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mamoshina P, Kochetov K, Cortese F, Kovalchuk A, Aliper A, Putin E, et al. Blood biochemistry analysis to detect smoking status and quantify accelerated aging in smokers. Sci Rep. 2019;9:142.
Article
PubMed
PubMed Central
Google Scholar
Putin E, Mamoshina P, Aliper A, Korzinkin M, Moskalev A, Kolosov A, et al. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging (Albany NY). 2016;8:1021–33.
Article
CAS
Google Scholar
Tsai I-J, Shen W-C, Lee C-L, Wang H-D, Lin C-Y. Machine learning in prediction of bladder cancer on clinical laboratory data. Diagnostics (Basel). 2022;12:203.
Article
Google Scholar
Cao Y, Hu Z-D, Liu X-F, Deng A-M, Hu C-J. An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters. Dis Markers. 2013;35:653–60.
Article
PubMed
PubMed Central
Google Scholar
Qu Y, Deng X, Lin S, Han F, Chang HH, Ou Y, et al. Using innovative machine learning methods to screen and identify predictors of congenital heart diseases. Front Cardiovasc Med. 2021;8:797002.
Article
PubMed
Google Scholar
Kurstjens S, de Bel T, van der Horst A, Kusters R, Krabbe J, van Balveren J. Automated prediction of low ferritin concentrations using a machine learning algorithm. Clin Chem Lab Med. 2022. https://doi.org/10.1515/cclm-2021-1194.
Article
PubMed
Google Scholar
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(Suppl 1):S67-74.
Article
Google Scholar
Yang H, Luo Y, Ren X, Wu M, He X, Peng B, et al. Risk prediction of diabetes: big data mining with fusion of multifarious physical examination indicators. Inf Fusion. 2021;75:140–9.
Article
Google Scholar
Chen H, Hu L, Li H, Hong G, Zhang T, Ma J, et al. An Effective machine learning approach for prognosis of paraquat poisoning patients using blood routine indexes. Basic Clin Pharmacol Toxicol. 2017;120:86–96.
Article
CAS
PubMed
Google Scholar
Lu M, Fan Z, Xu B, Chen L, Zheng X, Li J, et al. Using machine learning to predict ovarian cancer. Int J Med Inform. 2020;141:104195.
Article
PubMed
Google Scholar
Muhsen IN, Shyr D, Sung AD, Hashmi SK. Machine learning applications in the diagnosis of benign and malignant hematological diseases. CHI. 2020;3:13.
Article
Google Scholar
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;36:2057–61.
Article
PubMed
Google Scholar
Zhan J, Chen W, Cheng L, Wang Q, Han F, Cui Y. Diagnosis of asthma based on routine blood biomarkers using machine learning. Comput Intell Neurosci. 2020;2020:8841002.
Article
PubMed
PubMed Central
Google Scholar
Xiao J, Ding R, Xu X, Guan H, Feng X, Sun T, et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J Transl Med. 2019;17:119.
Article
PubMed
PubMed Central
Google Scholar
Carobene A, Milella F, Famiglini L, Cabitza F. 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. 2022. https://doi.org/10.1515/cclm-2022-0182.
Article
PubMed
Google Scholar
Brinati D, Campagner A, Ferrari D, Locatelli M, Banfi G, Cabitza F. Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study. J Med Syst. 2020;44:135.
Article
CAS
PubMed
PubMed Central
Google Scholar
Domínguez-Olmedo JL, Gragera-Martínez Á, Mata J, Pachón ÁV. Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation. J Med Internet Res. 2021;23:e26211.
Article
PubMed
PubMed Central
Google Scholar
Podnar S, Kukar M, Gunčar G, Notar M, Gošnjak N, Notar M. Diagnosing brain tumours by routine blood tests using machine learning. Sci Rep. 2019;9:14481.
Article
PubMed
PubMed Central
Google Scholar
Wu J, Zan X, Gao L, Zhao J, Fan J, Shi H, et al. A machine learning method for identifying lung cancer based on routine blood indices: qualitative feasibility study. JMIR Med Inform. 2019;7:e13476.
Article
PubMed
PubMed Central
Google Scholar
Li H, Lin J, Xiao Y, Zheng W, Zhao L, Yang X, et al. Colorectal cancer detected by machine learning models using conventional laboratory test data. Technol Cancer Res Treat. 2021;20:153303382110583.
Article
Google Scholar
Ford BA, McElvania E. Machine learning takes laboratory automation to the next level. J Clin Microbiol. 2020;58:e00012-20.
Article
PubMed
PubMed Central
Google Scholar
Rosenbaum MW, Baron JM. Using machine learning-based multianalyte delta checks to detect wrong blood in tube errors. Am J Clin Pathol. 2018;150:555–66.
Article
PubMed
Google Scholar
Farrell C-J. Identifying mislabelled samples: machine learning models exceed human performance. Ann Clin Biochem. 2021;58:650–2.
Article
PubMed
Google Scholar
Tamimi W, Martin-Ballesteros J, Brearton S, Alenzi FQ, Hasanato R. Evaluation of biological specimen acceptability in a complex clinical laboratory before and after implementing automated grading serum indices. Br J Biomed Sci. 2012;69:103–7.
Article
CAS
PubMed
Google Scholar
Farrell C-JL, Giannoutsos J. Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results. Int J Lab Hematol. 2022;44:497–503.
Article
PubMed
Google Scholar
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.
Article
CAS
PubMed
Google Scholar
Fang K, Dong Z, Chen X, Zhu J, Zhang B, You J, et al. Using machine learning to identify clotted specimens in coagulation testing. Clin Chem Lab Med. 2021;59:1289–97.
Article
CAS
PubMed
Google Scholar
Wilkes EH, Rumsby G, Woodward GM. Using machine learning to aid the interpretation of urine steroid profiles. Clin Chem. 2018;64:1586–95.
Article
CAS
PubMed
Google Scholar
Salama ME, Otteson GE, Camp JJ, Seheult JN, Jevremovic D, Holmes DR, et al. Artificial intelligence enhances diagnostic flow cytometry workflow in the detection of minimal residual disease of chronic lymphocytic leukemia. Cancers. 2022;14:2537.
Article
CAS
PubMed
PubMed Central
Google Scholar
Katayev A, Fleming JK, Luo D, Fisher AH, Sharp TM. Reference intervals data mining: no longer a probability paper method. Am J Clin Pathol. 2015;143:134–42.
Article
PubMed
Google Scholar
Yang D, Su Z, Zhao M. Big data and reference intervals. Clin Chim Acta. 2022;527:23–32.
Article
CAS
PubMed
Google Scholar
Ma C, Zou Y, Hou L, Yin Y, Zhao F, Hu Y, et al. Validation and comparison of five data mining algorithms using big data from clinical laboratories to establish reference intervals of thyroid hormones for older adults. Clin Biochem. 2022;S0009–9120(22):00137.
Google Scholar
Poole S, Schroeder LF, Shah N. An unsupervised learning method to identify reference intervals from a clinical database. J Biomed Inform. 2016;59:276–84.
Article
PubMed
Google Scholar
LaRocco MT, Franek J, Leibach EK, Weissfeld AS, Kraft CS, Sautter RL, et al. Effectiveness of preanalytic practices on contamination and diagnostic accuracy of urine cultures: a laboratory medicine best practices systematic review and meta-analysis. Clin Microbiol Rev. 2016;29:105–47.
Article
PubMed
Google Scholar
Íñigo M, Coello A, Fernández-Rivas G, Carrasco M, Marcó C, Fernández A, et al. Evaluation of the SediMax automated microscopy sediment analyzer and the Sysmex UF-1000i flow cytometer as screening tools to rule out negative urinary tract infections. Clin Chim Acta. 2016;456:31–5.
Article
PubMed
Google Scholar
Burton RJ, Albur M, Eberl M, Cuff SM. Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Med Inform Decis Mak. 2019;19:171.
Article
PubMed
PubMed Central
Google Scholar
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-40.
Article
CAS
Google Scholar
Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M-M, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev. 2021;41:1427–73.
Article
PubMed
Google Scholar
Zhou Q, Qi S, Xiao B, Li Q, Sun Z, Li L. Artificial intelligence empowers laboratory medicine in industry 4.0. Nan Fang Yi Ke Da Xue Xue Bao. 2020;40:287–96.
PubMed
Google Scholar
Salinas M, Flores E, Lopez-Garrigós M, Salinas CL. Artificial intelligence: a step forward in the clinical laboratory, a decision maker hub. Clin Biochem. 2022;S0009-9120(22)00134-5.
Greaves RF, Bernardini S, Ferrari M, Fortina P, Gouget B, Gruson D, et al. Key questions about the future of laboratory medicine in the next decade of the 21st century: a report from the IFCC-emerging technologies division. Clin Chim Acta. 2019;495:570–89.
Article
CAS
PubMed
Google Scholar
Dai W, Ke P-F, Li Z-Z, Zhuang Q-Z, Huang W, Wang Y, et al. Establishing classifiers with clinical laboratory indicators to distinguish COVID-19 from community-acquired pneumonia: retrospective cohort study. J Med Internet Res. 2021;23:e23390.
Article
PubMed
PubMed Central
Google Scholar
Alaidarous MA. The emergence of new trends in clinical laboratory diagnosis. Saudi Med J. 2020;41:1175–80.
Article
PubMed
PubMed Central
Google Scholar
Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial intelligence and mapping a new direction in laboratory medicine: a review. Clin Chem. 2021;67:1466–82.
Article
PubMed
Google Scholar
Paranjape K, Schinkel M, Hammer RD, Schouten B, Nannan Panday RS, Elbers PWG, et al. The value of artificial intelligence in laboratory medicine. Am J Clin Pathol. 2021;155:823–31.
Article
PubMed
Google Scholar
Ardon O, Schmidt RL. Clinical laboratory employees’ attitudes toward artificial intelligence. Lab Med. 2020;51:649–54.
Article
PubMed
Google Scholar
Cabitza F, Campagner A, Soares F, García de Guadiana-Romualdo L, Challa F, Sulejmani A, et al. The importance of being external. Methodological insights for the external validation of machine learning models in medicine. Comput Methods Programs Biomed. 2021;208:106288.
Article
PubMed
Google Scholar
Carobene A, Aarsand AK, Bartlett WA, Coskun A, Diaz-Garzon J, Fernandez-Calle P, et al. The European Biological Variation Study (EuBIVAS): a summary report. Clin Chem Lab Med. 2022;60:505–17.
Article
CAS
PubMed
Google Scholar
Demirci F, Akan P, Kume T, Sisman AR, Erbayraktar Z, Sevinc S. Artificial neural network approach in laboratory test reporting: learning algorithms. Am J Clin Pathol. 2016;146:227–37.
Article
CAS
PubMed
Google Scholar
Johnson PR, Shahangian S, Astles JR. Managing biological variation data: modern approaches for study design and clinical application. Crit Rev Clin Lab Sci. 2021;58:493–512.
Article
PubMed
Google Scholar
Borovecki A, Mlinaric A, Horvat M, Supak SV. Informed consent and ethics committee approval in laboratory medicine. Biochem Med (Zagreb). 2018;28:030201.
Article
Google Scholar
Gronowski AM, Budelier MM, Campbell SM. Ethics for laboratory medicine. Clin Chem. 2019;65:1497–507.
Article
CAS
PubMed
Google Scholar
Gruson D, Helleputte T, Rousseau P, Gruson D. Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin Biochem. 2019;69:1–7.
Article
PubMed
Google Scholar
Pennestrì F, Banfi G. Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key-points. Clin Chem Lab Med. 2022.
Véliz C. Medical privacy and big data: A further reason in favour of public universal healthcare coverage. In: de Campos TC, Herring J, Phillips AM, editors. Philosophical foundations of medical law [Internet]. Oxford (UK): Oxford University Press; 2019 [cited 2022 Jun 16]. http://www.ncbi.nlm.nih.gov/books/NBK550264/.
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.
Article
Google Scholar