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.
Patients are turning to AI to interpret lab results, but accuracy concerns and lack of validation are raising new challenges for clinical laboratories.
Artificial intelligence (AI) is rapidly reshaping how patients engage with diagnostic test results, creating new challenges and opportunities for clinical laboratories.
A growing number of consumers are now turning to AI tools to interpret their lab reports, according to a recent article from Mashable, often before consulting a physician. Startups and wellness companies are capitalizing on this demand by offering subscription-based services that translate complex lab data into simplified summaries and suggested next steps.
For lab professionals, this trend reflects a broader shift toward patient-driven data interpretation.
However, the underlying technology remains largely unvalidated for clinical use. Current AI models are not specifically benchmarked for interpreting laboratory results, and there is no standardized framework to measure accuracy at scale. Early evidence suggests these tools can misinterpret biomarkers, overlook key findings, or generate unreliable recommendations—raising concerns about downstream clinical impact. The article featured quotes from John Whyte, MD, MPH, CEO of the American Medical Association.
“Physicians are [not always] the best communicators,” Whyte said. “I wish we were, and [that we] had more time.” (Photo credit: American Medical Association)
He noted that there is currently no strong clinical evidence showing AI can reliably interpret blood test results or generate accurate, personalized health recommendations. As a result, it remains unclear whether these paid AI services offer any advantage over free chatbots—or even traditional physician guidance.
“I think you have to be skeptical about some of the claims,” Whyte noted.
Some developers are attempting to mitigate risk by layering in clinician review and structured validation processes. In many cases, AI is being positioned as a support tool rather than a diagnostic authority, focused on improving health literacy rather than delivering medical advice.
Still, the lack of peer-reviewed data and proven outcomes continues to be a major limitation. Experts caution that errors may be more likely in complex cases, where misinterpretation could lead to unnecessary testing, delayed diagnoses, or increased patient anxiety.
Wide Pricing Spectrum Highlights Unclear Value and Market Opportunity
Pricing for AI-driven lab result interpretation varies widely, reflecting a fragmented and still-evolving market. At the low end, some platforms offer freemium models or charge just a few dollars per report or month for basic explanations, with subscriptions typically ranging from about $4 to $8 per month for more advanced insights. At the higher end, wellness-focused companies bundle AI interpretation with lab testing and clinician review, charging hundreds of dollars annually—often $199 or more per test or roughly $500 per year for ongoing biomarker tracking.
Enterprise and lab-facing solutions follow a different model, using pay-per-report or per-biomarker pricing, sometimes costing only cents per analyte but scaling significantly with volume. For clinical laboratories, this wide pricing spectrum underscores both the commercial opportunity and the uncertainty around value, as cost does not yet correlate clearly with validated clinical performance.
A hazy aspect that Dark Daily editors want to note is whether a given AI tool used for interpreting test results has been cleared by the Food and Drug Administration. Not surprisingly, there is a regulatory gap given how quickly AI is evolving, and consumers may not be reading the fine print from software developers about FDA oversight. Generally, the FDA would consider any software providing interpretation of a diagnosis to be a medical device.
For clinical laboratories, the rise of AI-driven result interpretation highlights the need to adapt. Clearer reporting, improved patient communication, and more accessible digital tools will be critical as patients increasingly seek to understand their results independently. While AI may enhance engagement, laboratories remain essential in ensuring accuracy, clinical context, and appropriate use of diagnostic information.
Labcorp and the Children’s Hospital of Philadelphia are partnering to accelerate pediatric diagnostic innovation and national access, signaling new growth opportunities for clinical laboratories in high-complexity, specialty testing markets.
This effort could potentially reshape how clinical laboratories access and deliver advanced testing for younger patient populations.
For clinical laboratory professionals, the partnership signals a growing emphasis on
scaling niche, high-complexity diagnostics through national infrastructure. By combining CHOP’s pediatric research and clinical expertise with Labcorp’s commercialization capabilities and broad testing network, the organizations plan to build a joint innovation pipeline designed to move new assays from discovery to nationwide availability more efficiently.
Labcorp–CHOP Collaboration Targets Pediatric Testing Gap
This model addresses a longstanding gap in the diagnostics market. Pediatric-specific tests, which account for developmental and physiological differences, have historically lagged behind adult-focused diagnostics. The collaboration targets key growth areas including oncology, metabolic disease, autoimmune disorders, and rare diseases—segments that increasingly require advanced molecular and genetic testing capabilities.
“Our shared aim to improve children’s health makes this collaboration so powerful,” Stephen R. Master, division chief and director of metabolic and advanced diagnostics at CHOP, said in a news release. “By pairing CHOP’s pediatric leadership with Labcorp’s nationwide reach, we seek to deliver important new and specialized tests to children and their families more efficiently and at greater scale.” (Photo credit: CHOP)
From a business perspective, the agreement reflects a broader industry trend toward partnerships that bridge academic innovation with commercial scale. For clinical laboratories, it underscores the opportunity and competitive pressure to expand pediatric test menus, invest in specialized capabilities, and align with research institutions to accelerate time to market.
The agreement also illustrates Labcorp’s continuing effort to expand its presence in the US diagnostics market. Labcorp and chief competitor Quest Diagnostics have been on buying sprees in recent years to grab laboratory outreach businesses from health systems. The CHOP partnership represents a different business avenue for Labcorp to head into.
As demand grows for precision diagnostics in younger populations, collaborations like this may define the next phase of growth in the clinical lab industry, particularly in high-value, specialty testing segments.
A long-term study shows increasing rates of therapy-related AML as cancer survival improves, pushing clinical laboratories to expand genomic testing, enhance surveillance, and prepare for more complex secondary malignancies.
A new population-based study published in CANCER, a journal of the American Cancer Society, signals a growing diagnostic and surveillance challenge that clinical laboratories should take note of. Rates of therapy-related acute myeloid leukemia (tAML), a secondary blood cancer linked to prior chemotherapy and radiation exposure, are rising.
Researchers analyzing data from the Osaka Cancer Registry found that tAML incidence increased steadily between 1990 and 2020. Among nearly 10,000 AML cases, 6.5% were therapy-related, with incidence rising from 0.13 to 0.36 per 100,000 people. The proportion of tAML within total AML cases nearly doubled over the study period, reflecting a shifting disease burden tied to improved cancer survival.
“The study provides an important step towards better understanding how the nature of tAML is changing with the increasing number of cancer survivors,” said lead author Kenji Kishimoto, MD, PhD, of the Osaka International Cancer Institute.
For clinical laboratories, the findings underscore the downstream impact of modern oncology treatments. As more patients survive primary cancers, labs are increasingly likely to encounter complex secondary malignancies requiring advanced hematologic testing, molecular profiling, and longitudinal monitoring. tAML, in particular, is associated with prior DNA damage from cytotoxic therapies, often presenting with aggressive clinical features and distinct genetic signatures.
The study also highlights changing patterns in primary cancers preceding tAML. While prior blood cancers remained the most common precursor, cases following breast cancer treatment rose notably over time, suggesting evolving risks tied to treatment regimens and survivorship trends. Colorectal and gastric cancers were also represented, though gastric cancer–associated cases declined.
For lab professionals, this trend reinforces the need to adapt testing strategies, expand genomic capabilities, and collaborate closely with oncology teams as therapy-related malignancies become a more visible component of routine diagnostic workflows.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
Set for April 28–29 in New Orleans, the 31st Annual Executive War College will bring lab leaders together for practical, execution-focused strategies across reimbursement, staffing, compliance, and emerging technologies, with new emphasis on digital pathology and AI-driven operations.
The 31st Annual Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management, April 28–29 in New Orleans, will bring together clinical laboratory leaders to address the most pressing challenges shaping the industry in 2026. This year’s event emphasizes practical, execution-focused strategies across financial performance, workforce development, compliance, and emerging technologies.
A key addition to the 2026 program is the inaugural Executive Forum on Digital Pathology Management, a dedicated session exploring digital workflows, artificial intelligence (AI), and data integration. Designed as an interactive and collaborative experience, the forum will highlight real-world implementation strategies and provide attendees with actionable insights into adopting new technologies.
Recently, the Dark Report highlighted what’s to come at the event. Further, Dark Daily reported on key sessions that attendees won’t want to miss.
Six Major Themes Shaping the Industry
The conference agenda is structured around six strategic themes reflecting the evolving laboratory landscape.
Financial strategy sessions will focus on improving reimbursement, strengthening payer relationships, and using analytics to drive revenue growth.
Workforce discussions will address staffing shortages, automation, and leadership development.
Compliance sessions will offer frameworks for managing regulatory risk and embedding compliance into daily operations.
Innovation and technology will play a central role, with case studies demonstrating how laboratories can leverage molecular diagnostics, automation, and informatics to enhance clinical value and operational efficiency.
AI will receive particular attention, with sessions examining both its opportunities and challenges, including governance, validation, and return on investment.
Additionally, experts will explore trends in mergers and acquisitions and strategic partnerships, providing guidance on growth, valuation, and long-term positioning.
Healthcare attorney Elizabeth Sullivan of McDonald Hopkins leads a panel discussion at last year’s Executive War College. Sullivan will return for two sessions at the upcoming 2026 conference. (Photo credit: EWC)
2025 Executive War College Highlights
Workforce challenges persist in 2026 and will again be a key theme at the event. The 2025 Executive War College highlighted several innovative approaches to staffing.
For example, the Dark Report reported on a 2025 Executive War College presentation by Jennifer Fralick, vice president anatomic pathology and clinical laboratories at Stanford Health Care. Fralick noted that clinical labs are addressing severe staffing shortages by focusing on internal talent development through career ladders, training programs, and smarter staffing models that shift routine tasks away from licensed professionals. These strategies improve efficiency, reduce burnout, and help labs build sustainable, long-term workforce pipelines instead of relying solely on external hiring. (Fralick is returning to this year’s event to discuss an AI playbook for labs.)
Operational solutions will also be highlighted in the 2026 agenda. Last year, as the Dark Report noted in an article, Shashirekha Shetty, PhD, professor in the Department of Pathology at Case Western University, presented on how up to 70% of laboratory errors occur in the pre-analytical phase, often due to incorrect test orders, improper sample handling, and poor communication, making it a major risk to patient care and lab efficiency. Shetty emphasized that labs must take full ownership of this phase by implementing standardized workflows, strengthening training and collaboration with clinicians, and embedding pre-analytic quality into their overall quality management systems.
Attendees can expect updated solutions for these challenges and more presented by experts at this year’s Executive War College, which is just a short month away. With nearly 80 sessions and around 150 speakers, the program is designed to equip attendees with practical tools, real-world case studies, and operational playbooks. Laboratory executives will leave with clear, actionable roadmaps to navigate financial pressures, regulatory scrutiny, and rapid technological change.
Johns Hopkins researchers show that measuring DNA methylation variability can improve early cancer detection accuracy and strengthen liquid biopsy performance across diverse patient populations.
Researchers at Johns Hopkins Kimmel Cancer Center are advancing a new approach to liquid biopsy that could improve early cancer detection by focusing on variability in DNA methylation patterns—rather than absolute levels—offering a potentially more reliable biomarker across diverse patient populations.
Dark Daily’s sibling publication Today’s Clinical Labreported that the liquid biopsy market is expected to increase by approximately 20% between 2022 and 2032, noting early cancer detection as a driver of the increase.
The method introduces a novel metric called the Epigenetic Instability Index (EII), designed to measure random variation, or “stochasticity,” in DNA methylation. In a proof-of-concept study published in Clinical Cancer Research, the approach demonstrated strong performance in distinguishing patients with early-stage cancers from healthy individuals.
“This is the first study where we are trying to really implement measuring that variation, or stochasticity, into a diagnostic tool,” said lead author Hariharan Easwaran, PhD. “We immediately found that measuring DNA methylation variation performs better than just measuring DNA methylation by itself.”
Model Targets Methylation Variability to Improve Multi-Cancer Detection
Traditional methylation-based liquid biopsies typically rely on detecting fixed changes at specific genomic sites. However, those tests are often developed using narrow patient cohorts and can struggle to generalize across broader populations. By contrast, the EII approach aims to capture a more universal biological signal tied to early tumor development.
To build the model, researchers analyzed more than 2,000 publicly available DNA methylation samples and identified 269 genomic regions (CpG islands) that capture the majority of methylation variability across cancer types.
“We identified specific genomic regions that tend to be the most variable in DNA methylation marks during cancer,” said first author Sara-Jayne Thursby, a postdoctoral researcher in Easwaran’s lab. “In cell-free DNA in the blood, that variability shouldn’t be high, but if it is, it is indicative of a developing cancerous phenotype.”
Using these regions, the team trained a machine learning model that demonstrated high accuracy across multiple cancers. In lung adenocarcinoma, the test detected stage 1A disease with 81% sensitivity at 95% specificity. For early-stage breast cancer, sensitivity reached approximately 68% at the same specificity level. The tool also showed potential utility in colon, pancreatic, brain, and prostate cancers.
Researchers say the findings support the idea that epigenetic instability may be an early hallmark of cancer progression.
“We hypothesize that early-stage tumors and precancerous lesions that exhibit high degrees of methylation variation… may be more resistant to intrinsic cancer-protective mechanisms and progress more rapidly,” said co-lead author Thomas Pisanic, PhD.
Looking ahead, the team plans to further validate the EII in larger clinical studies and position it as a complementary tool alongside existing screening methods. Easwaran noted that the test could serve as a “secondary triaging measure,” helping clinicians determine whether follow-up procedures—such as biopsies—are necessary after inconclusive or false-positive screening results.
For clinical laboratories, the approach signals a growing shift toward more nuanced, data-driven biomarkers that may improve early detection while reducing unnecessary procedures.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
