Prospects for the implementation of collaborative robotic systems in laboratory medicine: a review

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Abstract

Laboratory diagnostics is one of the key domains of modern medicine, providing up to 80% of all clinical decision-making. The growing volume of laboratory testing, workforce transformation, and the need to reduce error rates make automation particularly relevant. Traditional total laboratory automation systems demonstrate high throughput; however, their economic and organizational effectiveness is limited by complex integration and high implementation costs. In this context, increasing attention is being given to collaborative robots (cobots) capable of performing preanalytical and logistical tasks in direct interaction with personnel. Despite the high technological potential, ready-to-use solutions for clinical laboratories remain scarce, underscoring the scientific and practical relevance of this review.

The analytical material was collected through a scientific data search in the PubMed database covering the period from 1985 to 2025. The bibliometric analysis included 2247 publications, of which 961 were published within the last five years, reflecting the rapid growth of interest in medical robotics.

The analysis demonstrated that the implementation of cobots in laboratory workflows leads to a reduction in sample processing time by 30%–50%, a decrease in error rates by 60%–80%, and increased productivity with minimal costs associated with personnel training. The automation of the preanalytical phase, where the proportion of errors may reach up to 70%, is critically important. Practical examples of the use of collaborative robots in microbiological and serological studies confirm their effectiveness and flexibility compared with traditional systems.

The future application of collaborative robotics is associated with the integration of artificial intelligence, digital twins, and self-learning algorithms, paving the way toward fully autonomous laboratory platforms. Successful implementation will require a stepwise strategy, interface standardization, and interdisciplinary collaboration among professionals in medicine, engineering, and information technology. Under these conditions, cobots may become a cornerstone of future laboratory diagnostics, enhancing quality, cost-effectiveness, and sustainability.

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About the authors

Andrey G. Komarov

Moscow Scientific and Practical Center for Laboratory Research

Email: komarovag@zdrav.mos.ru
ORCID iD: 0009-0000-8597-7125
SPIN-code: 8442-5834
Russian Federation, Moscow

Pavel P. Tregub

Central Research Institute of Epidemiology; The First Sechenov Moscow State Medical University

Author for correspondence.
Email: tregub@cmd.su
ORCID iD: 0000-0002-3650-6121
SPIN-code: 9606-5198

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow; Moscow

Vadlen V. Tyulyubaev

The First Sechenov Moscow State Medical University

Email: vadlen.secha@gmail.com
ORCID iD: 0009-0006-2547-6162
SPIN-code: 7568-3209
Russian Federation, Moscow

Pavel O. Bochkov

Moscow Scientific and Practical Center for Laboratory Research

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

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Arcadiy S. Goldberg

Russian Medical Academy of Continuous Professional Education

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

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Vasiliy G. Akimkin

Central Research Institute of Epidemiology

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

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. Diagram of keyword occurrence in PubMed publications for 1985–2025 on the topic of “laboratory and robotics”. The diagram was constructed using VOSviewer v. 1.6.20 and a dataset containing 2247 publications from the PubMed database, which was collected using the search query (("laborator*"[Title/Abstract]) AND ("Robot*"[Title/Abstract]) OR ("Robot"[Title/Abstract])). Different colors in the diagram indicate common clusters of keywords, which were formed by the program using neural network algorithms for correlating terms with publication trends. The size of the round colored markers is directly proportional to the number of episodes of co-occurrence of various keywords from the titles or abstracts of the publication with other keywords. The lines in the diagram represent the interrelations of keywords that co-occur in publications from the dataset.

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3. Fig. 2. Diagram of keyword occurrence in PubMed publications for 1985–2025 on the topic of “laboratory diagnostics and robots”. The diagram was constructed using VOSviewer v. 1.6.20 and a dataset containing 94 publications from the PubMed database, which was collected using the search query (("Clinical Chemistry"[Title/Abstract]) OR ("Clinical laborator*"[Title/Abstract]) AND ("Robot*"[Title/Abstract]) OR ("Robot "[Title/Abstract]) OR ("collaborative robot*"[Title/Abstract])). Different colors in the diagram indicate common keyword clusters formed by the program using neural network algorithms for correlating terms with publication trends. The size of the circular colored markers is directly proportional to the number of times different keywords from the publication titles or abstracts co-occur with other keywords. The lines in the diagram represent the relationships between keywords that co-occur in publications from the dataset.

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Note

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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