A multicenter trial of Vitestro’s system shows strong performance and patient acceptance, pointing to gains in preanalytical efficiency and specimen quality.
A newly published multicenter clinical study signals a potential shift in how clinical laboratories approach one of the most labor-intensive steps in the diagnostic workflow, namely phlebotomy.
Vitestro announced results from its Autonomous Blood Drawing Optimization and Performance Testing (ADOPT) trial, published in Clinical Chemistry, evaluating the performance, safety, and patient experience of its fully autonomous robotic phlebotomy system, Aletta.
In March, Dark Daily reported that Vitestro raised $70 million in Series B funding to accelerate development and commercialization of its robotic phlebotomy system, as clinical laboratories look to automation to address staffing shortages, improve workflow efficiency, and standardize blood collection quality. Later that month, Dark Daily’s sibling publication, The Dark Report, followed up the announcement with an analysis on what this means for business operations in the clinical lab.
Vitestro funded the study. Several study authors disclosed they are employees of Vitestro and hold stock options or equity in the company, while others also hold equity stakes.
Robotic Phlebotomy Shows Strong Performance and Workflow Gains
The study—conducted across several leading healthcare institutions in the Netherlands with additional patient acceptance data from the US—represents one of the first peer-reviewed, real-world evaluations of robotic blood collection in routine clinical practice.
For clinical laboratory professionals, the findings highlight growing momentum around automation in the preanalytical phase, an area historically prone to variability and operational inefficiencies.
The study included 1,633 patients across three outpatient phlebotomy settings and reported that the automated system had a 94.5% first-stick success rate when a suitable vein was identified. Performance remained strong across traditionally challenging patient populations, including those with high BMI (97.4%), difficult venous access (92.7%), and elderly patients (93.4%). Hemolysis rates were reported at 0.3%, and adverse events at 0.6%, both lower than rates typically associated with manual blood draws. All adverse events were classified as mild.
From a laboratory operations perspective, these metrics suggest potential improvements in specimen quality and reduced need for redraws. These factors directly impact workflow efficiency, turnaround time, and overall cost of care.
Equally notable for labs focused on patient-centered care, the study found that 90% of patients reported less, similar, or far less pain compared to manual phlebotomy, while 82% said they would prefer or were open to using the robotic system in the future. A separate US-based patient acceptance study found that 86% of patients were willing to use the technology.
Automation Moves Upstream as Labs Eye Preanalytical Standardization
“This multicenter study represents a significant milestone in the clinical validation of autonomous robotic phlebotomy in routine practice,” said Robert de Jonge, PhD, professor and head of the Department of Laboratory Medicine at Amsterdam University Medical Center. “The demonstration of strong performance and safety outcomes is critical to building clinical and laboratory confidence in this new approach. As laboratories advance automation across the diagnostic workflow, innovations like Aletta in the preanalytical phase will be instrumental in enabling more standardized, scalable, and integrated care delivery.”
The implications for clinical labs extend beyond performance metrics. As workforce shortages persist and demand for diagnostic testing continues to grow, automated solutions in specimen collection could help alleviate staffing pressures while improving consistency.
“From a laboratory perspective, consistency in the preanalytical phase is critical, yet often difficult to achieve in daily practice,” said Thijs van Holten, PhD, clinical chemist at St. Antonius Hospital. “Aletta introduces a standardized approach to diagnostic blood collection, with the potential to reduce variability, improve sample quality, and support more reliable diagnostic outcomes.” (Photo credit: St. Antonius Hospital)
While further validation and broader deployment will be needed, the study positions robotic phlebotomy as an emerging tool for labs seeking to modernize operations and reduce preanalytical errors.
For clinical laboratory professionals, the takeaway is clear: automation is moving upstream, and the preanalytical phase may be the next frontier for innovation, standardization, and scalable growth.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
A top public hospital CEO says AI could soon take over radiology functions to cut costs—raising similar automation questions for clinical labs—though critics warn the technology is not ready to replace physicians.
The CEO of NYC Health + Hospitals says his system is prepared to begin replacing radiologists with artificial intelligence (AI) in certain use cases, once regulatory barriers are addressed. From a clinical lab professional perspective, health systems are actively evaluating where AI can reduce reliance on highly trained specialists while maintaining diagnostic throughput.
“We could replace a great deal of radiologists with AI at this moment, if we are ready to do the regulatory challenge,” Katz said.
AI as a Cost and Workflow Strategy
Katz noted that AI could expand access to screening—particularly in breast cancer—while lowering operational costs. One proposed model would shift radiologists into a secondary review role, validating only abnormal findings flagged by AI.
For clinical laboratories, this mirrors ongoing discussions around digital pathology, AI-assisted test interpretation, and automated workflows in areas such as hematology, microbiology, and molecular diagnostics. If imaging adopts a “AI-first, specialist-second” model, similar expectations could follow in the lab.
This approach could deliver what Katz described as “major savings,” particularly for large systems facing staffing shortages and increasing test volumes.
“For women who aren’t considered high risk, if the test comes back negative, it’s wrong only about 3 times out of 10,000,” Lubarsky said, adding that the technology is “actually better than human beings.” (Photo credit: Westchester Medical Center Health Network)
Katz also questioned whether regulations should evolve to allow AI to interpret imaging independently—potentially establishing a precedent that could influence how regulators approach AI in laboratory medicine.
Why Clinical Labs Should Pay Attention
While the discussion centers on radiology, the underlying drivers—cost containment, workforce shortages, and demand for faster turnaround times—are identical pressures facing clinical laboratories.
If regulators permit AI to operate with reduced physician oversight in imaging, labs could see accelerated adoption of AI-driven decision support, automated result interpretation, and even reduced hands-on review in certain testing workflows.
At the same time, the debate highlights a key risk of balancing efficiency gains with diagnostic accuracy and patient safety.
Pushback Raises Safety Concerns
Not all healthcare professionals agree with the direction. Some radiologists warn that current AI tools are not ready for independent clinical use.
“Undeniable proof that confidently uninformed hospital administrators are a danger to patients: easily duped by AI companies that are nowhere near capable of providing patient care,” said Mohammed Suhail, MD, of North Coast Imaging.
“Any attempt to implement AI-only reads would immediately result in patient harm and death, and only someone with zero understanding of radiology would say something so naive.”
The debate signals what may be ahead for the broader diagnostics industry. As health systems test AI-driven models in radiology, clinical laboratories may soon face similar expectations to leverage automation for cost savings—while defending the continued role of expert oversight in ensuring quality and patient safety.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
New funding will help Vitestro expand clinical validation, scale manufacturing, and prepare its autonomous blood-draw technology for broader hospital and laboratory adoption.
Vitestro has secured $70 million in oversubscribed Series B financing to accelerate development and commercialization of its Aletta Autonomous Robotic Phlebotomy Device (ARPD), a platform designed to automate routine blood collection in clinical settings.
In March 2025, The Dark Reportreported on Vitestro’s development of the ARPD—one of the last manual steps in the laboratory testing workflow. The company had recently received CE mark approval in Europe and reported clinical trial results showing a 95% first-stick success rate and strong patient acceptance, positioning the technology as a potential solution to phlebotomy staffing shortages and pre-analytical variability in clinical laboratories.
For clinical laboratory leaders facing persistent staffing shortages and rising specimen volumes, the investment highlights growing industry interest in automating one of healthcare’s most common clinical procedures.
“Closing our Series B financing reflects strong conviction in our mission to establish a new standard in autonomous robotic venous access and diagnostic blood collection,” said Toon Overbeeke, chief executive officer and co-founder of Vitestro. “Diagnostic blood collection remains the highest-volume invasive medical procedure globally, with billions of procedures performed annually.” (Photo credit: Vitestro)
Vitestro plans to use the capital to advance the next generation of its Aletta platform, conduct additional clinical studies, and scale manufacturing as the company prepares for broader commercial rollout in Europe and eventual entry into the United States through the FDA’s de novo regulatory pathway.
The Aletta system combines multimodal imaging, robotics, and artificial intelligence to autonomously identify veins, guide needle insertion, and collect blood samples with high precision. The platform is designed to perform routine diagnostic blood draws, potentially helping laboratories standardize collection quality while reducing human-dependent variability.
For laboratory executives, the technology could offer a new strategy to stabilize phlebotomy operations as workforce shortages persist. By automating routine blood draws, robotic platforms like Aletta may allow organizations to improve workflow predictability, support existing staff, and maintain patient throughput in high-volume outpatient settings.
A forthcoming issue of The Dark Report will feature interviews with Vitestro executives and provide deeper analysis of what autonomous robotic phlebotomy could mean for clinical laboratories, including its potential impact on staffing, workflow efficiency, and specimen quality.
