The integration of artificial intelligence (AI) into healthcare and laboratory medicine is reshaping diagnostics, workflows, and patient management. Yet, technological progress alone cannot ensure meaningful outcomes. The concept of Beneficial Intelligence (BI), defined as the synergy of human and artificial intelligence (H + A = B), emphasizes that technology must be guided by human purpose, ethics, and empathy. BI reframes AI not as a replacement for human expertise but as an augmentation that enables laboratory professionals to deliver care that is accurate, sustainable, and patient-centered. In alignment with value-based healthcare, BI prioritizes outcomes that matter most-clinical, operational, economic, and societal. Laboratory medicine provides a fertile ground for this framework, where digitalization, automation, and machine learning models already enhance diagnostics, risk stratification, and decision support. However, responsible adoption requires validation against patient outcomes, adherence to structured evaluation frameworks and continuous human oversight. Ultimately, Beneficial Intelligence is not only a technical model but a mindset: a commitment to ensure that the alliance of human wisdom and AI fosters equitable, efficient, and sustainable healthcare for the future.

Although measurement of thyroid hormones (THs), TSH and thyroid autoantibodies, is usually straightforward, erroneous results are occasionally generated, raising the possibility of incorrect or delayed diagnoses, unwarranted investigations, and provision of inappropriate treatments. Assay interference can be acquired (e.g. antibodies), genetic (e.g. variant TH-binding proteins), or pharmaceutical (e.g. displacing agents and biotin) in nature. In those with discordant thyroid function, assay interference must be excluded prior to the diagnosis of rare inherited (resistance to thyroid hormone) or tumoural conditions (TSHoma). Liaison between the clinician and clinical biochemistry teams is essential when assay interference is being investigated; appropriate further biochemical testing depends on the clinical picture, initial biochemistry, and local biochemical expertise. In a subset of cases, access to a regional centre where patients can be clinically assessed, in conjunction with performance of specialised clinical laboratory testing (biochemical, +/- genetic), may be required. When assay interference is identified, clear communication with the patient (ideally aided by reliable, accessible patient literature) and documentation to their physician are necessary. Professionals must stay updated on advances in testing and be alert for evolving assay interferences. An accompanying plain language summary is intended to provide an accessible overview of the key messages and recommendations contained in this guideline for non-specialist readers, including patients, carers, healthcare professionals outside the speciality, and the wider public.

OBJECTIVES:Despite growing interest in artificial intelligence (AI) and machine learning (ML), many laboratory professionals lack experience with developing in-house AI systems or implementing those supplied by external providers. The IFCC Committee on AI in Laboratory Medicine (C-AILM) conducted a survey to collect the status of AI/ML applications, challenges, and expert perspectives on key technical considerations. METHODS:An 20-item survey was distributed to laboratory professionals experienced in AI. It covered application status (in-house or provider-supplied, with or without regulatory approval); essential information to request from AI system providers; validation or verification practices; monitoring strategies; and perceived implementation challenges. RESULTS:Fifty complete responses from global experts were received. AI implementation in clinical laboratories was limited and heterogeneous. Most respondents agreed that AI systems provided externally, regardless of regulatory approval status, require local verification. Key information needed from providers included performance metrics from original and external datasets, and demographics of the training/test populations. For both approved and non-approved models, high-priority verification studies were local performance analysis, confirmation of intended-use alignment, and verification of privacy and security safeguards. Top monitoring strategies were regular accuracy checks and comparison against human decision-making. Leading challenges were insufficient IT infrastructure and lack of practical implementation guidelines. CONCLUSIONS:Although many challenges remain, clinical laboratories demonstrate strong enthusiasm for AI, particularly with the growing prevalence of commercial AI products. The timely expert insights from our survey and C-AILM recommendations for both AI system providers and clinical laboratories on essential information, verification requirements, and monitoring strategies will inform standardized guideline development.

BACKGROUND:Hypertensive disorders of pregnancy, such as preeclampsia and/or gestational hypertension, are major contributors to maternal morbidity and future cardiovascular risk. However, their acute role has heretofore been unstudied. METHODS:We evaluated peripartum hs-cTnI dynamics and compared phenotype-specific patterns among preeclampsia, hypertension, and normotensive controls in a retrospective cohort study. Leftover serum or EDTA plasma samples collected within ±48 hours of delivery were analyzed. Mixed-effects linear regression models assessed log10-transformed hs-cTnI concentrations and temporal slopes, adjusting for demographic and obstetric covariates. RESULTS:We included 609 participants (788 samples), comprising 90 preeclampsia, 116 hypertension, and 403 normotensive control patients. Median hs-cTnI concentrations were <2.5 ng/L prior to delivery across all groups and increased significantly after delivery. The greatest post-delivery rise occurred in preeclampsia, followed by hypertension and normotensive controls. In multivariable analyses, hs-cTnI levels were higher in PEC compared with normotensive controls (β=0.266, P<0.001) and hypertension (β=0.160, P=0.011), with hypertension also higher than normotensive controls (β=0.107, P=0.046). Peripartum hs-cTnI slopes were steeper in preeclampsia compared with normotensive controls (β=0.005/h, P=0.013). Slopes did not differ between preeclampsia and hypertension. Increases > 99th % URL were uncommon and occurred more often in preeclampsia. CONCLUSIONS:This study provides a systematic assessment of hs-cTnI trajectories during the immediate peripartum period, suggesting delivery is a physiologic cardiovascular "stress test". Preeclampsia is associated with greater myocardial stress compared with normotensive pregnancies, with patients with hypertension demonstrating intermediate changes. These data indicate that marked increases in hs-cTnI are rare during normal deliveries and deserve additional diagnostic evaluation.

BACKGROUND:Anti-Müllerian hormone (AMH) measurement is central to ovarian reserve assessment but remains affected by inter-method variability among immunoassays. Point-of-care testing (POCT) enables rapid and decentralized AMH testing; however, independent evaluation of analytical performance and agreement with routine laboratory methods is required prior to clinical implementation. OBJECTIVE:To evaluate the analytical performance, method agreement, and usability of a fluorescent-based point-of-care AMH assay using a bicentric study design. METHODS:Analytical precision and trueness were assessed at two independent sites using three levels of quality control material. Method comparison was performed on 304 anonymized residual serum samples from routine laboratory requests, including infertility assessment, polycystic ovary syndrome (PCOS) investigation, in vitro fertilization (IVF) monitoring, and cancer surveillance, covering a clinically relevant AMH range (0.1-13 ng/mL). Statistical analyses included Passing-Bablok regression, Spearman correlation, and Bland-Altman analysis. System usability was assessed using a structured questionnaire. RESULTS:A strong correlation was observed between the POCT AMH assay and the routine laboratory method (Spearman r = 0.967, p < 0.0001). Passing-Bablok regression showed a slope of 0.925 and an intercept of 0.213, indicating a proportional bias. Quality control imprecision ranged from 4.1% to 13.6% (CV), with positive biases between 2.9% and 17.8%. A concentration-dependent pattern was observed, with overestimation at low concentrations, increased deviation in the mid-range, and near-equivalence at higher values. Bland-Altman analysis confirmed greater dispersion at low-to-mid AMH levels. Usability evaluation indicated high ease of use and limited training requirements. CONCLUSIONS:The fluorescent-based AMH POCT assay demonstrated acceptable analytical performance, strong correlation with a routine laboratory method, and favourable usability. However, until harmonisation across methods is achieved, cautious interpretation and confirmatory laboratory testing remain essential when results are close to clinical decision thresholds.

Background:Heart failure is a frequent complication of diabetes, highlighting the importance of integrated cardiometabolic assessment. Glycated hemoglobin (HbA1c) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) represent complementary biomarkers reflecting glycemic control and myocardial stress. Point-of-care testing (POCT) platforms may facilitate decentralized diagnostics and shorten analytical turnaround time. The aim of this study was to evaluate the analytical performance of the AFIAS-3 point-of-care fluorescence immunoassay platform for HbA1c and NT-proBNP measurements in comparison with established central laboratory methods. Methods:This single-center verification study included 50 patient samples for each analyte. HbA1c measurements obtained using AFIAS-3 were compared with Tosoh G8 high-performance liquid chromatography (HPLC), while NT-proBNP measurements were compared with the cobas 8000 electrochemiluminescence immunoassay. Analytical precision and accuracy were assessed using manufacturer quality control materials. Method comparison was performed using Passing-Bablok regression, Bland-Altman analysis, and Spearman correlation. Agreement at clinical thresholds was evaluated using Cohen's kappa statistics. Results:For HbA1c, analytical precision was excellent, with coefficients of variation of 1.9% and 1.8%. Correlation with the reference method was strong (r = 0.98), and the mean bias was minimal (-0.03%). Agreement at the diagnostic threshold of 6.5% was excellent (κ = 0.92). For NT-proBNP, coefficients of variation were 5.3% and 15.4%. Correlation with the central laboratory method was very high (r = 0.99), although proportional bias was observed at higher concentrations (slope 1.24). Agreement at clinically relevant thresholds remained substantial. Conclusions:The AFIAS-3 POCT platform demonstrated excellent analytical agreement for HbA1c and acceptable agreement for NT-proBNP at clinically relevant thresholds. Dual biomarker POCT may support decentralized cardiometabolic diagnostics, although NT-proBNP values at higher concentrations should be interpreted cautiously.

Laboratory medicine is undergoing a profound transformation driven by advances in artificial intelligence (AI), automation, and data interoperability. By 2050, laboratories may evolve from analytical testing facilities into an interconnected health intelligence capable of translating biological, digital, and environmental data into more personalized, preventive, and sustainable healthcare solutions. Building on previous foresight analysis conducted by the IFCC Emerging Technologies Division (ETD), global healthcare outlook reports, and expert-informed horizon scanning, this paper explores ten megatrends that may shape laboratory medicine by mid-century. These include precision multi-omics, AI-supported diagnostics, distributed healthcare models, patient-owned data ecosystems, digital twins, convergence of imaging and laboratory medicine, population-wide prevention strategies, regenerative therapies, sustainability imperatives, and workforce transformation. Collectively, these developments represent a paradigm shift from data generation to data interpretation, positioning laboratories as trusted nodes within the health intelligence network connecting patients, clinicians, and healthcare systems. The integration of automation with advanced multi-omics technologies, including robotic liquid handling, autonomous LC-MS/MS platforms, spatial proteomics, and real-time metabolomics, will accelerate the transition toward precision and planetary health. Four exploratory scenarios illustrate plausible trajectories for laboratory medicine in 2050. The Hyper-Intelligent Laboratory explores the implications of self-learning systems and advanced analytics capable of supporting earlier disease prediction and intervention. The Sustainable and Regenerative Laboratory illustrates how environmental stewardship and diagnostic excellence may converge within circular healthcare ecosystems. The Patient-in-the-Loop Revolution examines a future in which citizens become active stewards of their health data and participants in healthcare decision-making. The Spacefaring Laboratory extends this reflection beyond Earth, illustrating how extreme environments may accelerate innovation in autonomous diagnostics, sustainability, and human health monitoring. By 2050, laboratory medicine could serve as a central intelligence layer within healthcare systems, transforming biological signals into actionable knowledge. Achieving this vision will require scientific innovation, regulatory adaptability, equitable access, ethical governance, sustainability, and a future-ready workforce.

Laboratory medicine lies at the core of modern healthcare, enabling timely diagnosis, effective patient monitoring, and increasingly personalized therapeutic strategies. Over the past decades, automation has profoundly reshaped the role of clinical laboratories, substantially enhancing their contribution to clinical outcomes, operational efficiency, and the overall sustainability of healthcare systems. More recently, laboratory automation has emerged as a cornerstone of value-based laboratory medicine, representing not merely a technological upgrade but a strategic transformation of laboratory practice aimed at delivering measurable value to patients and healthcare stakeholders. Although automation has long been established in clinical chemistry and immunoassays, its scope is now expanding to molecular diagnostics and mass spectrometry - two disciplines that are central to precision medicine. Looking ahead, the convergence of automation, digitalization, and artificial intelligence is driving the emergence of hyperautomation in laboratory medicine. Within this paradigm, laboratories evolve from isolated testing units into integrated diagnostic hubs, in which results from multiple laboratory disciplines are harmonized and contextualized to effectively support clinical decision-making.

Thyroid function tests are one of the most frequently requested laboratory investigations worldwide and a recognized area of over-testing. While inappropriate thyroid testing was traditionally discussed in terms of clinical relevance and economic burden, its environmental impact was largely overlooked. Each unnecessary test contributes to healthcare-related carbon emissions, through patient travel, blood collection, single-use consumables, reagent production, analyzer operating and waste management. In the context of growing commitments toward sustainable healthcare and net-zero emissions, laboratory medicine must reassess not only what is tested, but also why and how often. This article explores the intersection between clinical appropriateness and environmental sustainability in thyroid test ordering. Evidence-based strategies, including TSH-first and reflex testing algorithms, can substantially reduce unnecessary free hormone and antibody measurements while preserving diagnostic accuracy. Furthermore, contextualized reference intervals, particularly in elderly populations and during pregnancy, may reduce overdiagnosis and avoid repeated testing driven by physiological variations. Laboratory stewardship programs are therefore a pragmatic strategy for improving clinical quality while contributing to more sustainable healthcare delivery.

AIMS:Structural valve degeneration (SVD) is the leading cause of late bioprosthetic valve failure. Lipoprotein(a) [Lp(a)] contributes to native aortic valve calcification, but its role in SVD is unclear. We investigated whether elevated Lp(a) is associated with SVD after bioprosthetic aortic valve replacement (AVR) and whether this differs between stenotic and regurgitant phenotypes. METHODS AND RESULTS:We studied 174 bioprosthetic AVR patients with available Lp(a) levels over a median echocardiographic follow-up of 7.3 years (1372 studies). SVD was defined by VARC-3 criteria, and associations were analysed with Fine-Gray competing risk models. Lp(a) was evaluated categorically (≤ or > 125 nmol/L) and continuously using spline modelling. During follow-up, 40 patients developed SVD (22 stenotic, 9 mixed, and 9 regurgitant). The 15-year cumulative incidence was 51% with a median onset at 14.8 years. Elevated Lp(a) was associated with a higher risk of overall SVD (62% vs. 47%; SHR 2.06, 95% CI 1.09-3.91; P = 0.026) and specifically with stenotic/mixed phenotypes (SHR 2.57, 95% CI 1.26-5.23; P = 0.009). No association was observed with regurgitant phenotypes (SHR 0.85, 95% CI 0.19-3.92; P = 0.84). After multivariable adjustment, elevated Lp(a) remained an independent predictor of stenotic/mixed SVD (adjusted SHR 3.00, 95% CI 1.48-6.07; P = 0.002). Spline modelling showed a linear dose-response, with each 25 nmol/L increase in Lp(a) conferring 13% higher risk. CONCLUSION:Elevated Lp(a) is independently associated with long-term risk of stenotic/mixed SVD. These findings highlight Lp(a) as a promising biomarker of prosthetic valve vulnerability and support investigation of emerging Lp(a)-lowering therapies to improve valve durability.

Technological innovation in laboratory medicine is advancing rapidly, driven by artificial intelligence, next-generation sequencing, high-resolution mass spectrometry, novel biomarkers, and decentralized point-of-care testing. However, the translation of these advances into routine clinical practice remains uneven worldwide. To explore current barriers and priorities, the IFCC Emerging Technologies Division conducted a multi-regional survey among laboratory leaders and functional unit representatives. The survey identified a persistent gap between innovation and implementation. Major challenges included unequal access to advanced diagnostics, financial constraints, regulatory complexity, and workforce preparedness. Respondents highlighted disparities in infrastructure and expertise, particularly in low- and middle-income settings, raising concerns about widening global health inequities. Economic sustainability emerged as a central barrier, with high capital costs, uncertain reimbursement pathways, and the need for robust evidence of clinical utility and cost-effectiveness. Regulatory requirements, including evolving frameworks such as IVDR, were perceived as increasing compliance burdens and potentially limiting availability of specialized tests. A significant proportion of respondents also emphasized the need for structured implementation support, including training programs (64.7%), improved access to resources (64.7%), and strengthened networking and partnerships (66.7%). A significant skills gap related to artificial intelligence, data science, and bioinformatics was also reported, emphasizing the need for competency-based education and interdisciplinary collaboration. Six strategic priority domains were identified: AI integration, genomics capacity, global harmonization, decentralized diagnostics, biomarker validation, and implementation science. Bridging innovation and implementation will require coordinated global leadership, structured support from scientific organizations, and sustained investment to ensure equitable, clinically impactful adoption of emerging technologies.

OBJECTIVE:Integrated interpretation of renal function and natriuretic peptides is essential in ambulatory cardio-renal care. This study evaluated whether a single-capillary dual point-of-care testing (POCT) strategy combining creatinine/eGFR and NT-proBNP could support real-time, integrated clinical decision-making during a single consultation. METHODS:In this exploratory, single-center, real-world study that was designed as a feasibility study focusing on workflow integration and clinical usability, capillary creatinine/eGFR and NT-proBNP were evaluated using a single-fingerstick dual-POCT workflow in two exploratory clinical subgroups recruited according to their respective clinical indications. Analytical agreement was assessed using Passing-Bablok regression (slope, intercept, and 95% confidence intervals) and Bland-Altman analysis. Agreement at guideline-based clinical thresholds (eGFR ≤60 mL/min/1.73m2; NT-proBNP ≥125 ng/L) was evaluated using Cohen's kappa. Workflow feasibility and time-to-result were also documented. RESULTS:Forty-three patients underwent creatinine/eGFR testing, and a separate subgroup of 22 patients underwent NT-proBNP testing, reflecting different clinical indications and real-world recruitment during this exploratory feasibility study. Passing-Bablok regression showed good agreement with slopes close to unity and no significant systematic bias. Bland-Altman analysis demonstrated clinically acceptable limits of agreement. Diagnostic agreement at clinical thresholds was substantial for eGFR (κ=0.71) and perfect for NT-proBNP (κ=1.00), with no clinically relevant misclassification. Dual biomarker results were available within approximately 15 minutes, enabling same-visit clinical decisions. CONCLUSIONS:A single-capillary dual-biomarker POCT workflow enables rapid and reliable cardio-renal assessment within one ambulatory visit. Its primary value lies in integrated, threshold-based clinical interpretation enabled by the simultaneous availability of complementary biomarkers, rather than analytical comparison alone.

This collective opinion paper, based on presentations delivered during the two-day European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Strategic Conference 2026, "Laboratory Medicine for Society" (Prague, Czech Republic, 24-25 April 2026) offers a forward-looking overview of the Federation's strategic priorities, ongoing initiatives, and future projects aimed at enhancing the value, visibility, and societal contribution of laboratory medicine, thereby ensuring a bright and sustainable future for the profession. By generating actionable information that supports clinical decision-making across the entire continuum of care, laboratory medicine contributes substantially to improving patient outcomes, enhancing healthcare efficiency, and promoting population health.

BACKGROUND Advances in congenital heart disease (CHD) management have improved survival rates, resulting in a growing population of women of childbearing age with CHD. These women face higher risk of obstetric and neonatal complications during pregnancy. While the underlying mechanisms remain unclear, previous studies have identified maternal vascular malperfusion (MVM) in their placentas. OBJECTIVES This study aimed to compare the prevalence of MVM in pregnant women with CHD to those without CHD, assess its association with obstetric and neonatal outcomes, and explore potential risk factors for MVM. METHODS In this prospective single-center study, we enrolled pregnant women with CHD who were followed from March 2021 to June 2023, along with a control group matched for age, parity, and body mass index. Placentas were analyzed for MVM using a scoring system based on the Amsterdam Placental Workshop Group Consensus guidelines. N-terminal pro b-type natriuretic peptide assays in the second trimester and echocardiography in the third trimester were performed to evaluate maternal cardiovascular health. RESULTS Placentas from 39 CHD and 67 control women were analyzed. MVM prevalence was significantly higher in the CHD group compared to controls (56.4% vs 13.4%, P < 0.001). CHD pregnancies had a higher incidence of adverse obstetric and neonatal outcomes, which were independently associated with MVM (RR: 7.2, P 1/4 0.002). No clinical or paraclinical factors were associated with MVM in CHD women. CONCLUSIONS Women with CHD had a higher prevalence of MVM compared to controls, which was associated with adverse pregnancy outcomes. However, no clinical or paraclinical risk factors for MVM were identified. (JACC Adv. 2025;4:101592) (c) 2025 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).