Robotic Laboratory Automation: A Scoping Review of Feasibility, Costing, Planning, and Design Strategies

Introduction Automated robotic laboratory systems bring together robotics, artificial intelligence, and informatics to support various laboratory processes. This scoping review aimed to map the extent, nature, and distribution of published evidence on the feasibility, costing, planning, and designing of these systems.

Methods The review was conducted according to Joanna Briggs Institute methodology and PRISMA-ScR guidelines. The review included any type of laboratory (population), automated robotic technologies used in laboratory work (concept), and aspects related to feasibility, costing, planning, or designing (context). We searched PubMed, MEDLINE via PubMed and Embase for English-language peer-reviewed articles published after 2015. Two independent reviewers screened 509 records using Rayyan software. Fifteen studies ultimately met the inclusion criteria. Data were charted and synthesised thematically.

Results The included studies consisted of case studies, quantitative evaluations, mixed-methods analyses, and reviews from the fields of clinical microbiology, pathology, chemistry, and general laboratory automation. These studies represented settings in India, North America, Europe, and Asia. Evidence on feasibility highlighted track-based systems, integration of AI with Internet of Things, decision-tree approaches suitable for smaller laboratories, and advanced concepts such as knowledge graphs and digital twins. Costing studies emphasised the importance of full life-cycle costing models that consider maintenance and reagents, demonstrating that return on investment is often achieved through labour savings and reduced errors. Planning recommendations focused on interdisciplinary collaboration, use of decision-support tools, lean improvement events, and standardising chain-of-custody processes. Designing findings pointed to modular platforms, collaborative robots, user-programmable interfaces, and effective integration of liquid handlers with analytical software.

Conclusion The evidence shows meaningful improvements in turnaround time and laboratory throughput, particularly in high-volume settings. However, significant gaps remain, especially in low- and middle-income countries, long-term costing data, and evaluations of emerging fully autonomous systems. Future research should focus on multi-centre studies and the development of practical planning frameworks to support more equitable and effective adoption of robotic laboratory automation