Automated Biomedical Research Laboratories: Development, Current State, and a Roadmap for Adaptation into Shared Research Resources

Automated biomedical research laboratories (ABRLs) integrate robotics, laboratory instrumentation, orchestration software, and increasingly, machine learning and large language models to plan, execute, and interpret experiments with limited human intervention. Over roughly two decades, these systems have progressed from stand-alone liquid handlers and high-throughput screening workcells to remotely operated “cloud” laboratories, synthetic-biology biofoundries, and closed-loop “self-driving” laboratories capable of autonomous hypothesis generation and testing. This review summarizes the development and current state of ABRLs across biomedical domains, proposes a levels-of-autonomy framework, and evaluates which classes of ABRL are most readily adaptable into institutional shared research resources (core facilities). We maintain that standardized high-throughput sample-preparation and screening workcells, remotely accessible cloud platforms, and design–build–test–learn biofoundry services are the strongest near-term candidates for the core model, whereas bespoke self-driving laboratories are better suited to highly specialized cores or consortium-scale infrastructure. We outline a staged adaptation roadmap including needs assessment and governance, pilot deployment, workflow standardization with rigorous validation and quality management, informatics/remote-access and cost-recovery integration, and federation, and we address the workforce, change-management, and biosecurity-governance factors that determine success. Finally, we propose an evaluation framework that extends an eight-domain core performance model with automation-specific indicators for reliability, reproducibility, cost per result, access equity, and responsible use. Thoughtful adaptation of ABRLs offers core facilities a path to higher throughput, improved rigor and data provenance, and broader access, if standardization, workforce development, and governance keep pace.