Day, Patrick L.

Abstract Objectives Traditional laboratory regulatory frameworks provide robust guidance for conventional clinical testing but lack requirements for data documentation for artificial intelligence and machine learning (AI/ML) solutions. This gap creates significant risks since AI solutions directly inherit patterns, biases, and limitations from their training data. Here, we describe the development and implementation of a comprehensive data documentation checklist with accompanying data lineage for use within an AI lifecycle framework for clinical laboratories. Methods Building on the previously established Clinical AI Readiness Evaluator (CARE) framework, we developed a comprehensive data checklist and data lineage methodology through a multiphase process, including (1) comprehensive review of existing AI/ML data documentation frameworks, (2) focused meetings with 3 institutional AI operations teams, and (3) 3 rounds of iterative refinement by our multidisciplinary team. The checklist’s effectiveness was then assessed using 3 diverse AI/ML projects moving through the CARE framework. Results The CARE Data Checklist and Data Lineage provide a structured approach to documenting critical aspects of datasets used in AI/ML projects, including data composition, demographics, collection methods, labeling processes, usage constraints, maintenance requirements, and a data readiness assessment. The checklist addresses unique data-centric challenges of AI/ML applications, facilitating transparency, reproducibility, and regulatory compliance. Conclusions The CARE Data Checklist and Data Lineage serve as both a technical guide and a communication tool bridging gaps between technical and clinical stakeholders. By working on these documents early in the AI lifecycle, laboratories can anticipate and address data-related challenges, ultimately saving time, optimizing resources, and improving the reliability of AI-augmented laboratory solutions.

Abstract Background Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an “AI label feedback loop” in a clinically implemented AI-augmented kidney stone composition test. Methods An AI-augmented kidney stone composition test (V1) has been previously deployed for clinical kidney stone characterization. After several years of clinical use, a retrained model (V2) was developed using 6 times more data. Model performance of both V1 and V2 were evaluated across 3 datasets: a recent production validation (hold-out) set (mostly AI-influenced labels), the original V1 validation set (pre-AI, entirely human-labeled), and a subset of recent cases with exclusively human-generated or human-corrected labels. Results V2 demonstrated a 10% lower concordance rate than V1 when evaluated on the recent production hold-out set, despite a much larger training dataset. Performance between V1 and V2 was similar when applied to the pre-AI validation set. Notably, V2 outperformed V1 on the recent subset of cases with human-only or human-corrected labels, particularly for less-common stone types. These findings revealed an AI label feedback loop, confounding retraining and evaluation. Conclusions The integration of AI into clinical practice can potentially influence reported test results, complicating the development and evaluation of future models. To mitigate AI label feedback loops, ongoing human annotation and careful validation set construction are essential. These strategies can ensure reliable performance assessment and support the safe evolution of clinical AI systems.

Abstract Objective Despite exponential growth in artificial intelligence (AI) research for laboratory medicine and pathology, a significant gap exists between model development and clinical AI implementation. This article introduces a structured framework, the Clinical AI Readiness Evaluator (CARE), to bridge this gap and support the responsible adoption of AI in clinical laboratory settings. Methods Building upon the Machine Learning Technology Readiness Levels framework, we developed CARE specifically for the clinical laboratory environment by incorporating health care–specific requirements, regulatory considerations, and workflow integration needs. This framework was iteratively refined through practical application across diverse AI use cases within laboratory medicine and pathology. Results The CARE framework provides a systematic approach to AI development and implementation through 8 component workstreams: clinical use case, data, data pipeline, code, clinical user experience, clinical technology infrastructure, clinical orchestration, and regulatory compliance. Unlike generic AI frameworks, CARE distinguishes itself by emphasizing both health care and laboratory workflow integration, regulatory requirements, ethical considerations, and comprehensive validation for clinical contexts. The framework accommodates both internally developed models and commercial AI solutions, providing clear guidance through technology readiness levels and structured review processes. Conclusions The CARE framework addresses the unique challenges of implementing AI in laboratory medicine and pathology by providing a comprehensive roadmap from initial concepts through clinical deployment and maintenance. This article, the first in a series of 4, establishes the foundational AI lifecycle framework, while subsequent articles will explore data documentation, ethical AI considerations, and governance structures. By adopting this structured approach, laboratories can responsibly harness AI’s potential to enhance diagnostic accuracy and operational efficiencies and, ultimately, improve patient care.

Abstract Metals and metalloids including cobalt, gadolinium, lutetium, and germanium are used in numerous medical applications spanning diverse specialities including orthopedics, radiology, oncology, and healthcare artificial intelligence. These medical advances include cobalt containing orthopedic implants, gadolinium-based contrast agents, lutetium-containing cancer drugs, and germanium-based semiconductors. While these metal and metalloid-based solutions do improve patient care, there is a heavy side to how the elements needed for these solutions are mined, extracted, and discarded. These practices often exploit and harm vulnerable populations and environments. As healthcare professionals, we should be aware of the entire mineral to medicine lifecycle. As providers and consumers of these metal and metalloid-based solutions, we must advocate for more responsible and ethical extraction and recycling practices. As researchers and educators, we must promote and support continued research and development into less resource-intense medical solutions that can both improve patient care and sustainability.

Abstract Background We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented kidney stone composition test system for potential improvements in test quality, efficiency, cost-effectiveness, and staff satisfaction. Methods We compared quality, efficiency, staff satisfaction, and financial data from the 6 months after the AI-augmented laboratory test system was deployed (test period) with data from the same 6-month period in the previous year (control period) to determine if AI-augmentation improved key performance indicators of this laboratory test. Results In the 6 months following the deployment (test period) of the AI-augmented kidney stone composition test system, 44 830 kidney stones were analyzed. Of these, 92% of kidney stones were eligible for AI-assisted interpretation. Out of these AI-eligible stones, 45% were able to be auto-released by the AI-augmented test system without human secondary review. Furthermore, the new AI-augmented kidney stone test system resulted in an apparent 40% reduction in incorrect laboratory results. Additionally, the new AI-augmented test system improved laboratory efficiency by 20%, improved staff satisfaction, and reduced the average analysis cost per kidney stone by $0.23. Conclusions The AI-augmented test system improved test quality, efficiency, cost-effectiveness and staff satisfaction related to this kidney stone composition test.

Abstract Background A dual filtration-based method for determination of serum labile bound copper (LBC) and LBC fraction (LBC/total copper) was developed. Reduced total copper, elevated LBC, and elevated LBC fraction have been reported in Wilson disease (WD). Methods To evaluate the diagnostic performance of these markers, samples were obtained from 21 WD treatment-naïve (WD-TN, no WD treatment or <28 days of treatment) patients, 46 WD standard-of-care–treated (WD-SOC) patients, along with 246 patients representing other potential disorders of copper status. These were then compared to 213 reference interval population patients. Results Receiver operating characteristic curves for the reference population vs WD-TN yielded areas under the curve for total copper, LBC, and LBC fraction, of 0.99, 0.81, and 0.98, respectively. Using Youden cutoffs, sensitivity/specificity for WD-TN was 95%/97% for total copper, 71%/85% for LBC, and 95%/94% for LBC fraction. LBC values, but not total copper and LBC fraction, differed substantially between WD-TN and WD-SOC cohorts. We propose a dual model wherein total copper and LBC fraction results must agree to be classified as a “positive” or “negative” result for WD. This correctly classified 19/21 WD-TN patients as positive, and 194/213 reference interval patients as negative. The remaining “indeterminate” patients (representing approximately 9% of the reference and the WD-TN populations) exhibited conflicting total copper and LBC fraction results. When indeterminate results are excluded, this model exhibited apparent 100% sensitivity/specificity. Conclusions Agreement of total serum copper and LBC fraction classification may constitute an effective “rule-in” and “rule-out” assessment for WD-TN patients.

Context.— Clinical testing for Wilson disease (WD) is potentially challenging. Measuring the fraction of labile bound copper (LBC) to total copper may be a promising alternative diagnostic tool with better sensitivity and specificity than some current biomarker approaches. A dual filtration–based inductively coupled mass spectrometry (ICP-MS) assay to measure LBC in serum was developed. Objective.— To establish a reference interval for LBC and LBC to total copper (LBC fraction) in a healthy adult population, and to examine associations between total copper, LBC, and LBC fraction with age, sex, menopausal status, hormone replacement therapy, and supplement use. Design.— Serum samples were collected from healthy male (n = 110) and female (n = 104) patients between the ages of 19 and 80 years. Total copper and LBC were analyzed using ICP-MS. Results were used to calculate the LBC fraction. Reference intervals were calculated for the 2.5th and 97.5th percentiles for both LBC and LBC fraction. Results.— The reference intervals for LBC were determined to be 13 to 105 ng/mL and 12 to 107 ng/mL for female and male patients, respectively. The reference intervals for the LBC fraction were 1.0% to 8.1% and 1.2% to 10.5% for female and male patients, respectively. No significant associations were found regarding age, menopausal status, hormone replacement therapy, or vitamin and supplement use. Conclusions.— Sex-specific reference intervals have now been established for LBC and LBC fraction. These data in conjunction with further testing of WD populations can be used to assess the sensitivity and specificity of LBC fraction in screening, monitoring, and diagnosis.

Abstract Background Kidney stones are a highly prevalent disease worldwide. Additionally, both environmental and occupational exposure to Pb and Cd continue to be prevalent globally and can result in renal toxicity. The objective of this study was to examine the potential presence of Pb and Cd in kidney stones, and to assess for correlation with demographic factors including smoking, gender, age, and kidney stone matrix composition. Methods Patient kidney stones (n = 96) were analyzed using Fourier transform infrared spectroscopy to identify the stone constituents. Cd and Pb concentrations (µg/g) were determined by inductively coupled plasma mass spectrometry. Cd and Pb concentrations were correlated using bivariable and multivariable statistical analysis with demographic factors (age, gender, smoking status), and kidney stone composition. Results Kidney stone Cd (median 0.092 µg/g, range 0.014 to 2.46) and Pb concentrations (median 0.95 µg/g, range 0.060 to 15.4) were moderately correlated (r = 0.56, P < 0.0001). Cd concentrations were positively associated with patient history of smoking, patient age, and calcium oxalate monohydrate levels while negatively associated with struvite and uric acid/uric acid dihydrate. Pb concentrations were positively associated with females and apatite levels while negatively associated with uric acid/uric acid dihydrate. After holding constant other stone type composition levels, smoking status, and age, both Pb and Cd were positively associated with apatite and negatively associated with uric acid/uric acid dihydrate, struvite, and calcium carbonate. Conclusions Cd and Pb kidney stone concentrations are associated with specific kidney stone types. Cd and Pb kidney stone concentrations are both associated with smoking.