Prospects for the introduction of collaborative robotic systems in laboratory medicine



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About the authors

Andrey G. Komarov

Moscow Scientific and Practical Laboratory Research Center

Email: komarovag@zdrav.mos.ru
ORCID iD: 0009-0000-8597-7125
SPIN-code: 8442-5834
Russian Federation, 115580, Russia, Moscow, Orekhovy blvd., 49, Building 1

Pavel P. Tregub

Central Scientific Research Institute of Epidemiology of Rospotrebnadzor;
Sechenov First Moscow State Medical University (Sechenov University)

Email: pfiza_asmu@mail.ru
ORCID iD: 0000-0002-3650-6121
SPIN-code: 9606-5198

Doctor of Medical Sciences, Professor
Russian Federation, 111123, Russia, Moscow, 3A Novogireevskaya str.; 119991, Russia, Moscow, 8 Trubetskaya str., building 2

Vadlen V. Tyulyubaev

Sechenov First Moscow State Medical University (Sechenov University)

Email: vadlen.secha@gmail.com
ORCID iD: 0009-0006-2547-6162
SPIN-code: 7568-3209
Russian Federation, 119991, Russia, Moscow, 8 Trubetskaya str., building 2

Pavel O. Bochkov

Moscow Scientific and Practical Laboratory Research Center

Email: bochkovpo@dcli.ru
ORCID iD: 0000-0001-8555-5969
SPIN-code: 5576-8174

MD, Cand. Sci. (Medicine)
Russian Federation, 115580, Russia, Moscow, Orekhovy blvd., 49, Building 1

Arkadiy S. Goldberg

Russian Medical Academy of Continuing Professional Education

Email: goldarcadiy@gmail.com
ORCID iD: 0000-0002-2787-4731
SPIN-code: 8854-0469

MD, Cand. Sci. (Medicine)
Russian Federation, 25993, Russia, Moscow, 2/1 Barrikadnaya str., building 1

Vasiliy G. Akimkin

Central Scientific Research Institute of Epidemiology of Rospotrebnadzor

Author for correspondence.
Email: vgakimkin@yandex.ru
ORCID iD: 0000-0003-4228-9044
SPIN-code: 4038-7455

MD, Dr. Sci. (Medicine), Professor

Russian Federation, 111123, Russia, Moscow, 3A Novogireevskaya str.

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ABSTRACT

Laboratory diagnostics is one of the key areas of modern medicine, providing up to 80% of all clinical decisions.The increasing volume of testing, workforce transformations and the need to reduce errors make automation tasks particularly relevant. While traditional total laboratory automation (TLA) systems demonstrate high performance, their economic and organizational efficiency is constrained by the complexity of integration and the high cost of implementation. In this context, collaborative robots (cobots), capable of performing pre-analytical and logistical tasks in direct interaction with personnel are attracting growing attention. Despite the significant potential of this technology, ready-to-use solutions for clinical laboratories remain scarce, underscoring both the research and practical relevance of this review.

The material for analysis was collected through a PubMed search covering the years 1985–2025. The bibliometric analysis included 2,247 articles, of which 961 were published in the last five years, reflecting the rapid increase in interest in medical robotics.

The results of the analysis indicate that the integration of cobots into laboratory workflows reduces sample processing time by 30–50%, decreases error rates by 60–80% and improves productivity with minimal staff training costs. Automation of the pre-analytical phase is of particular importance, as up to 70% of laboratory errors occur at this stage. Practical examples of cobot implementation in microbiological and serological testing confirm their efficiency and flexibility compared with traditional systems.

The prospects for collaborative robotics are closely linked to the integration of artificial intelligence, digital twins and self-learning algorithms, paving the way for fully autonomous laboratory platforms. Successful implementation will require a phased approach, interface standardization and interdisciplinary collaboration among experts in medicine, engineering and information technology. Under these conditions, cobots may become a cornerstone of the future of laboratory diagnostics, enhancing its quality, cost-effectiveness and resilience.


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