4. Albahra S, Gorbett T, Robertson S, D'Aleo G, Kumar SVS, Ockunzzi S, et al. 2023; Artificial intelligence and machine learning overview in pathology & laboratory medicine: a general review of data preprocessing and basic supervised concepts. Semin Diagn Pathol. 40:71–87. DOI:
10.1053/j.semdp.2023.02.002. PMID:
36870825.
5. Punchoo R, Bhoora S, Pillay N. 2021; Applications of machine learning in the chemical pathology laboratory. J Clin Pathol. 74:435–42. DOI:
10.1136/jclinpath-2021-207393. PMID:
34117102.
6. Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, et al. 2021; Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol. 74:409–14. DOI:
10.1136/jclinpath-2020-206908. PMID:
32763920.
7. Paranjape K, Schinkel M, Hammer RD, Schouten B, Nannan Panday RS, Elbers PWG, et al. 2021; The value of artificial intelligence in laboratory medicine. Am J Clin Pathol. 155:823–31. DOI:
10.1093/ajcp/aqaa170. PMID:
33313667. PMCID:
PMC8130876.
10. Bocquet F, Campone M, Cuggia M. 2022; The challenges of implementing comprehensive clinical data warehouses in hospitals. Int J Environ Res Public Health. 19:7379. DOI:
10.3390/ijerph19127379. PMID:
35742627. PMCID:
PMC9223495.
13. Undru TR, Uday U, Lakshmi JT, Kaliappan A, Mallamgunta S, Nikhat SS, et al. 2022; Integrating artificial intelligence for clinical and laboratory diagnosis - a review. Maedica (Bucur). 17:420–6. DOI:
10.26574/maedica.2022.17.2.420. PMID:
36032592. PMCID:
PMC9375890.
14. Mahmudova S. 2021; Study and comparative analysis of programming languages used for big data. Rev Inf Eng Appl. 8:1–9. DOI:
10.18488/journal.79.2021.81.1.9.
16. 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.
17. Yoon YA, Lee YW, Kim S, Lee K, Park HD, Chun S, et al. 2021; Standardization status of total cholesterol concentration measurement: analysis of Korean external quality assessment data. Ann Lab Med. 41:366–71. DOI:
10.3343/alm.2021.41.4.366. PMID:
33536354. PMCID:
PMC7884189.
18. Jeong TD, Cho EJ, Lee K, Lee W, Yun YM, Chun S, et al. 2021; Recent trends in creatinine assays in Korea: long-term accuracy-based proficiency testing survey data by the Korean association of external quality assessment service (2011-2019). Ann Lab Med. 41:372–9. DOI:
10.3343/alm.2021.41.4.372. PMID:
33536355. PMCID:
PMC7884186.
19. Nam Y, Lee JH, Kim SM, Jun SH, Song SH, Lee K, et al. 2022; Periodic comparability verification and within-laboratory harmonization of clinical chemistry laboratory results at a large healthcare center with multiple instruments. Ann Lab Med. 42:150–9. DOI:
10.3343/alm.2022.42.2.150. PMID:
34635608. PMCID:
PMC8548239.
22. 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.
24. Kelly BS, Judge C, Bollard SM, Clifford SM, Healy GM, Aziz A, et al. 2022; Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol. 32:7998–8007. DOI:
10.1007/s00330-022-08784-6. PMID:
35420305. PMCID:
PMC9668941.
26. Ma C, Wang X, Wu J, Cheng X, Xia L, Xue F, et al. 2020; Real-world big-data studies in laboratory medicine: current status, application, and future considerations. Clin Biochem. 84:21–30. DOI:
10.1016/j.clinbiochem.2020.06.014. PMID:
32652094.
27. Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. 2019; Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 29:1640–6. DOI:
10.1007/s00330-018-5601-1. PMID:
29980928.
30. Burns BL, Rhoads DD, Misra A. 2023; The use of machine learning for image analysis artificial intelligence in clinical microbiology. J Clin Microbiol. 61:e0233621. DOI:
10.1128/jcm.02336-21. PMID:
37395657. PMCID:
PMC10575257.
32. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. 2017; Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2:230–43. DOI:
10.1136/svn-2017-000101. PMID:
29507784. PMCID:
PMC5829945.
35. Ansari I, Arfat M, Malik M, Bansal R. 2023; A cross-sectional survey on an insight into the current perceptions of Indian radiologists, radiographers, radiology trainee & medical imaging students on the future impact of artificial intelligence (AI) on the profession. J Pharm Neg Results. 14:1686–99.
36. Sun L, Yin C, Xu Q, Zhao W. 2023; Artificial intelligence for healthcare and medical education: a systematic review. Am J Transl Res. 15:4820–8. PMID:
37560249. PMCID:
PMC10408516.
37. Khullar D, Casalino LP, Qian Y, Lu Y, Chang E, Aneja S. 2021; Public vs physician views of liability for artificial intelligence in health care. J Am Med Inform Assoc. 28:1574–7. DOI:
10.1093/jamia/ocab055. PMID:
33871009. PMCID:
PMC8279784.
38. Abràmoff MD, Tobey D, Char DS. 2020; Lessons learned about autonomous AI: finding a safe, efficacious, and ethical path through the development process. Am J Ophthalmol. 214:134–42. DOI:
10.1016/j.ajo.2020.02.022. PMID:
32171769.
39. Bazoukis G, Hall J, Loscalzo J, Antman EM, Fuster V, Armoundas AA. 2022; The inclusion of augmented intelligence in medicine: a framework for successful implementation. Cell Rep Med. 3:100485. DOI:
10.1016/j.xcrm.2021.100485. PMID:
35106506. PMCID:
PMC8784713.
40. Safi S, Thiessen T, Schmailzl KJ. 2018; Acceptance and resistance of new digital technologies in medicine: qualitative study. JMIR Res Protoc. 7:e11072. DOI:
10.2196/11072. PMID:
30514693. PMCID:
PMC6299231.