Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute

Journal of Health and Medical Sciences

ISSN 2622-7258

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open access

Published: 31 August 2022

Are Medical Technologists still needed in Medical Laboratories in a Technologically Advanced Future?

Emmalyn B. Cutamora, Kenneth C. Cortes, Joseph Andrew Pepito

Cebu Doctors’ University, Philippines

journal of social and political sciences
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Pages: 56-65

Keywords: Laboratory Automation, Artificial Intelligence, Robotics, Medical Technologists, Medical Laboratory Scientists


An emerging trend in modern medical laboratories is automation, and it is having a positive impact on the quality of service to patients and on the safety of medical laboratory staff. The use of automation in medical laboratories enable many tests by analytical instruments with minimal use of an analyst. These automated instruments result in increasing the capabilities of a laboratory to process more workload with minimum involvement of manpower. Total Laboratory Automation (TLA) has many advantages including workload reduction, less time spent per sample, increased number of tests done in less time, use of a smaller sample amount, decreased risks for human errors, and higher reproducibility and accuracy. With the future practice of medical technologists in a technologically advanced future in peril. What edge do medical technologists have over Artificial Intelligence and Robotics that would still make them essential in medical laboratories in a future that is technologically advanced?


  1. Ahuja, A. S. (2019) ‘The impact of artificial intelligence in medicine on the future role of the physician’, PeerJ. PeerJ Inc., 2019(10). doi: 10.7717/peerj.7702.

  2. Aita, A. et al. (2017) ‘Patient safety and risk management in medical laboratories: theory and practical application’, Journal of Laboratory and Precision Medicine. AME Publishing Company, 2, pp. 75–75. doi: 10.21037/jlpm.2017.08.14.

  3. Angeletti, S. et al. (2015) ‘Laboratory Automation and Intra-Laboratory Turnaround Time: Experience at the University Hospital Campus Bio-Medico of Rome’, Journal of Laboratory Automation. SAGE Publications Inc., 20(6), pp. 652–658. doi: 10.1177/2211068214566458.

  4. Araujo, T. et al. (2020) ‘In AI we trust? Perceptions about automated decision-making by artificial intelligence’, AI and Society. Springer, pp. 1–13. doi: 10.1007/s00146-019-00931-w.

  5. Archetti, C. et al. (2017) ‘Clinical laboratory automation: A case study’, Journal of Public Health Research. Page Press Publications, 6(1), pp. 31–36. doi: 10.4081/jphr.2017.881.

  6. Armbruster, D. A., Overcash, D. R. and Reyes, J. (2014) ‘Clinical Chemistry Laboratory Automation in the 21st Century - Amat Victoria curam (Victory loves careful preparation).’, The Clinical biochemist. Reviews. The Australian Association of Clinical Biochemists, 35(3), pp. 143–53. Available at: (Accessed: 29 June 2020).

  7. Bader, V. and Kaiser, S. (2019) ‘Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence’, Organization. SAGE Publications Ltd, 26(5), pp. 655–672. doi: 10.1177/1350508419855714.

  8. Bajwa, M. (2014) ‘Emerging 21st century medical technologies’, Pakistan Journal of Medical Sciences. Professional Medical Publications, 30(3), p. 649. doi: 10.12669/pjms.303.5211.

  9. Bayot, M. L. and Naidoo, P. (2020) Clinical Laboratory, StatPearls. Available at: (Accessed: 29 June 2020).

  10. Bhatt, R. D., Shrestha, C. and Risal, P. (2019) ‘Factors Affecting Turnaround Time in the Clinical Laboratory of the Kathmandu University Hospital, Nepal.’, EJIFCC. International Federation of Clinical Chemistry and Laboratory Medicine, 30(1), pp. 14–24. Available at: (Accessed: 29 June 2020).

  11. Buch, V. H., Ahmed, I. and Maruthappu, M. (2018) ‘Artificial intelligence in medicine: Current trends and future possibilities’, British Journal of General Practice. Royal College of General Practitioners, pp. 143–144. doi: 10.3399/bjgp18X695213.

  12. Burtis, C. A. (1995) ‘Technological trends in clinical laboratory science’, Clinical Biochemistry. Elsevier, pp. 213–219. doi: 10.1016/0009-9120(94)00075-7.

  13. Carey, R. B. et al. (2018) ‘Implementing a quality management system in the medical microbiology laboratory’, Clinical Microbiology Reviews. American Society for Microbiology, 31(3). doi: 10.1128/CMR.00062-17.

  14. Chung, H. J. et al. (2018) ‘Experimental fusion of different versions of the total laboratory automation system and improvement of laboratory turnaround time’, Journal of Clinical Laboratory Analysis. John Wiley and Sons Inc., 32(5). doi: 10.1002/jcla.22400.

  15. Cresswell, K., Cunningham-Burley, S. and Sheikh, A. (2018) ‘Health care robotics: Qualitative exploration of key challenges and future directions’, Journal of Medical Internet Research. Journal of Medical Internet Research, 20(7). doi: 10.2196/10410.

  16. Dash, S. et al. (2019) ‘Big data in healthcare: management, analysis and future prospects’, Journal of Big Data. SpringerOpen, 6(1), p. 54. doi: 10.1186/s40537-019-0217-0.

  17. Davenport, T. and Kalakota, R. (2019) ‘The potential for artificial intelligence in healthcare’, Future Healthcare Journal. Royal College of Physicians, 6(2), pp. 94–98. doi: 10.7861/futurehosp.6-2-94.

  18. David Hopper, L. (2016) ‘Automated Microsampling Technologies and Enhancements in the 3Rs’, ILAR Journal. Oxford Academic, 57(2), pp. 166–177. doi: 10.1093/ILAR/ILW020.

  19. Dear, K. (2019) ‘Artificial Intelligence and Decision-Making’, RUSI Journal. Routledge, 164(5–6), pp. 18–25. doi: 10.1080/03071847.2019.1693801.

  20. Delahunt, C. B. et al. (2015) ‘Automated microscopy and machine learning for expert-level malaria field diagnosis’, in Proceedings of the 5th IEEE Global Humanitarian Technology Conference, GHTC 2015. Institute of Electrical and Electronics Engineers Inc., pp. 393–399. doi: 10.1109/GHTC.2015.7344002.

  21. Dunjko, V. and Briegel, H. J. (2018) ‘Machine learning & artificial intelligence in the quantum domain: a review of recent progress’, Reports on Progress in Physics. IOP Publishing, 81(7), p. 074001. doi: 10.1088/1361-6633/AAB406.

  22. Edwards-Schachter, M. (2018) ‘The nature and variety of innovation’, International Journal of Innovation Studies. Elsevier BV, 2(2), pp. 65–79. doi: 10.1016/j.ijis.2018.08.004.

  23. Ellison, T. L. et al. (2018) ‘Implementation of total laboratory automation at a tertiary care hospital in Saudi Arabia: Effect on turnaround time and cost efficiency’, Annals of Saudi Medicine. King Faisal Specialist Hospital and Research Centre, 38(5), pp. 352–357. doi: 10.5144/0256-4947.2018.352.

  24. Groenier, M., Pieters, J. M. and Miedema, H. A. T. (2017) ‘Technical Medicine: Designing Medical Technological Solutions for Improved Health Care’, Medical Science Educator. Springer, 27(4), pp. 621–631. doi: 10.1007/s40670-017-0443-z.

  25. Hong, L. et al. (2019) ‘Big Data in Health Care: Applications and Challenges’, Data and Information Management. Walter de Gruyter GmbH, 2(3), pp. 175–197. doi: 10.2478/dim-2018-0014.

  26. Hosogaya, S. (2015) ‘Role of Medical Technologists’ Training in the Future’, Rinsho byori. The Japanese journal of clinical pathology, 63(1), pp. 137–140. Available at: (Accessed: 29 June 2020).

  27. Jiang, F. et al. (2017) ‘Artificial intelligence in healthcare: Past, present and future’, Stroke and Vascular Neurology. BMJ Publishing Group, pp. 230–243. doi: 10.1136/svn-2017-000101.

  28. Kaartemo, V. and Helkkula, A. (2018) ‘A Systematic Review of Artificial Intelligence and Robots in Value Co-creation: Current Status and Future Research Avenues’, Journal of Creating Value. SAGE Publications, 4(2), pp. 211–228. doi: 10.1177/2394964318805625.

  29. Kelly, C. J. et al. (2019) ‘Key challenges for delivering clinical impact with artificial intelligence’, BMC Medicine. BioMed Central Ltd., p. 195. doi: 10.1186/s12916-019-1426-2.

  30. Kersting, K. (2018) ‘Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines’, Frontiers in Big Data. Frontiers Media SA, 1, p. 6. doi: 10.3389/fdata.2018.00006.

  31. Kim, D. W. et al. (2019) ‘Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: Results from recently published papers’, Korean Journal of Radiology. Korean Radiological Society, 20(3), pp. 405–410. doi: 10.3348/kjr.2019.0025.

  32. Laï, M. C., Brian, M. and Mamzer, M. F. (2020) ‘Perceptions of artificial intelligence in healthcare: Findings from a qualitative survey study among actors in France’, Journal of Translational Medicine. BioMed Central Ltd., 18(1), p. 14. doi: 10.1186/s12967-019-02204-y.

  33. Lee, W.-S. et al. (2018) ‘Assessing Concordance With Watson for Oncology, a Cognitive Computing Decision Support System for Colon Cancer Treatment in Korea’, JCO Clinical Cancer Informatics. American Society of Clinical Oncology (ASCO), (2), pp. 1–8. doi: 10.1200/cci.17.00109.

  34. Lippi, G. and Da Rin, G. (2019) ‘Advantages and limitations of total laboratory automation: A personal overview’, Clinical Chemistry and Laboratory Medicine. De Gruyter, pp. 802–811. doi: 10.1515/cclm-2018-1323.

  35. Liu, C. et al. (2018) ‘Using artificial intelligence (watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: Feasibility study’, Journal of Medical Internet Research. Journal of Medical Internet Research, 20(9). doi: 10.2196/11087.

  36. Loh, E. (2018) ‘Medicine and the rise of the robots: A qualitative review of recent advances of artificial intelligence in health’, BMJ Leader. BMJ Publishing Group, pp. 59–63. doi: 10.1136/leader-2018-000071.

  37. McAdam, A. J. (2018) ‘Total laboratory automation in clinical microbiology: A micro-comic strip’, Journal of Clinical Microbiology. American Society for Microbiology. doi: 10.1128/JCM.00176-18.

  38. Mitchell, M. and Kan, L. (2019) ‘Digital Technology and the Future of Health Systems’, Health Systems and Reform. Taylor and Francis Inc., pp. 113–120. doi: 10.1080/23288604.2019.1583040.

  39. Mosadeghrad, A. M. (2014) ‘Factors Affecting Medical Service Quality.’, Iranian journal of public health. Tehran University of Medical Sciences, 43(2), pp. 210–20. Available at: (Accessed: 29 June 2020).

  40. National Research Council (2011) Prudent Practices in the Laboratory, Prudent Practices in the Laboratory. National Academies Press. doi: 10.17226/12654.

  41. Ortiz, G. B. and Hsiang, W. (2018) ‘Focus: Medical Technology: Medical Technology’, The Yale Journal of Biology and Medicine. Yale Journal of Biology and Medicine, 91(3), p. 203. Available at: (Accessed: 29 June 2020).

  42. Ozawa, K. et al. (1992) ‘The expanding role of robotics in the clinical laboratory’, Journal of Automatic Chemistry, 14(1), pp. 9–15. doi: 10.1155/S1463924692000038.

  43. Palanisamy, V. and Thirunavukarasu, R. (2019) ‘Implications of big data analytics in developing healthcare frameworks – A review’, Journal of King Saud University - Computer and Information Sciences. King Saud bin Abdulaziz University, pp. 415–425. doi: 10.1016/j.jksuci.2017.12.007.

  44. Panteghini, M. (2004) ‘The future of laboratory medicine: understanding the new pressures.’, The Clinical biochemist. Reviews. The Australian Association of Clinical Biochemists, 25(4), pp. 207–15. Available at: (Accessed: 29 June 2020).

  45. Patel, B. N. et al. (2019) ‘Human–machine partnership with artificial intelligence for chest radiograph diagnosis’, npj Digital Medicine. Springer Science and Business Media LLC, 2(1), pp. 1–10. doi: 10.1038/s41746-019-0189-7.

  46. Pohoryles, R. J. and Tommasi, D. (2017) ‘Innovation: society, research & technology’, Innovation. Routledge, pp. 385–387. doi: 10.1080/13511610.2017.1383730.

  47. Rai Dastidar, T. and Ethirajan, R. (2020) ‘Whole slide imaging system using deep learning-based automated focusing’, Biomedical Optics Express. The Optical Society, 11(1), p. 480. doi: 10.1364/boe.379780.

  48. Reddy, S. (2018) ‘Use of Artificial Intelligence in Healthcare Delivery’, in eHealth - Making Health Care Smarter. InTech. doi: 10.5772/intechopen.74714.

  49. Resources, H. and Administration, S. (2016) Allied Health Workforce Projections, 2016-2030: Medical and Clinical Laboratory Technologists. Available at: (Accessed: 29 June 2020).

  50. Rimac, V. et al. (2018) ‘Implementation of the Autovalidation Algorithm for Clinical Chemistry Testing in the Laboratory Information System’, Laboratory Medicine. Oxford Academic, 49(3), pp. 284–291. doi: 10.1093/LABMED/LMX089.

  51. Da Rin, G., Zoppelletto, M. and Lippi, G. (2016) ‘Integration of Diagnostic Microbiology in a Model of Total Laboratory Automation’, Laboratory Medicine. Oxford Academic, 47(1), pp. 73–82. doi: 10.1093/LABMED/LMV007.

  52. Royakkers, L. and van Est, R. (2015) ‘A Literature Review on New Robotics: Automation from Love to War’, International Journal of Social Robotics. Springer Netherlands, 7(5), pp. 549–570. doi: 10.1007/s12369-015-0295-x.

  53. von Schomberg, L. and Blok, V. (2019) ‘Technology in the Age of Innovation: Responsible Innovation as a New Subdomain Within the Philosophy of Technology’, Philosophy and Technology. Springer, pp. 1–15. doi: 10.1007/s13347-019-00386-3.

  54. Shah, P. et al. (2019) ‘Artificial intelligence and machine learning in clinical development: a translational perspective’, npj Digital Medicine. Springer Science and Business Media LLC, 2(1), pp. 1–5. doi: 10.1038/s41746-019-0148-3.

  55. Simoes, E. (2015) ‘Health information technology advances health care delivery and enhances research’, Missouri medicine. Missouri State Medical Association, 112(1), pp. 37–40. Available at: (Accessed: 29 June 2020).

  56. Sutton, R. T. et al. (2020) ‘An overview of clinical decision support systems: benefits, risks, and strategies for success’, npj Digital Medicine. Springer Science and Business Media LLC, 3(1), pp. 1–10. doi: 10.1038/s41746-020-0221-y.

  57. Tan, L. and Ong, K. (2002) ‘The Impact of Medical Technology on Healthcare Today’, Hong Kong Journal of Emergency Medicine. SAGE Publications, 9(4), pp. 231–236. doi: 10.1177/102490790200900410.

  58. Thimbleby, H. (2013) ‘Technology and the future of healthcare.’, Journal of public health research. PAGEPress, 2(3), p. e28. doi: 10.4081/jphr.2013.e28.

  59. Tozzoli, R. et al. (2015) ‘Automation, consolidation, and integration in autoimmune diagnostics’, Autoimmunity Highlights. Springer-Verlag Italia s.r.l. doi: 10.1007/s13317-015-0067-5.

  60. Vollmer, S. et al. (2020) ‘Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness’, The BMJ. BMJ Publishing Group, 368. doi: 10.1136/bmj.l6927.

  61. Walton, P. (2018) ‘Artificial Intelligence and the Limitations of Information’, Information. MDPI AG, 9(12), p. 332. doi: 10.3390/info9120332.

  62. Wood, J. (2002) ‘The role, duties and responsibilities of technologists in the clinical laboratory’, Clinica Chimica Acta. Elsevier, 319(2), pp. 127–132. doi: 10.1016/S0009-8981(02)00033-5.

  63. Wu, J. et al. (2018) ‘Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory’, SLAS TECHNOLOGY: Translating Life Sciences Innovation. SAGE Publications Inc., 23(6), pp. 631–640. doi: 10.1177/2472630318775311.

  64. Xing, F. et al. (2018) ‘Deep Learning in Microscopy Image Analysis: A Survey’, IEEE Transactions on Neural Networks and Learning Systems. Institute of Electrical and Electronics Engineers Inc., 29(10), pp. 4550–4568. doi: 10.1109/TNNLS.2017.2766168.

  65. Yang, Y. et al. (2020) ‘The diagnostic accuracy of artificial intelligence in thoracic diseases’, Medicine. Lippincott Williams and Wilkins, 99(7), p. e19114. doi: 10.1097/MD.0000000000019114.

  66. Yeo, C. P. and Ng, W. Y. (2018) ‘Automation and productivity in the clinical laboratory: Experience of a tertiary healthcare facility’, Singapore Medical Journal. Singapore Medical Association, pp. 597–601. doi: 10.11622/smedj.2018136.

  67. Zima, T. (2017) ‘Accreditation of Medical Laboratories-System, Process, Benefits for Labs’, Journal of Medical Biochemistry. Society of Medical Biochemists of Serbia and Montenegro, 36(3), pp. 231–237. doi: 10.1515/jomb-2017-0025.