Testing and Diagnostics Library
Every article, presentation, spotlight, and news item we've tagged to Testing and Diagnostics.
Showing 73–96 of 314
AI pathology deployment improves cancer diagnostic access in Asia
AstraZeneca and Roche Diagnostics are partnering to deploy AI-assisted pathology across nine Asian markets, targeting improved diagnostic accuracy and access to precision cancer care where disease burden is highest. The initiative combines computational pathology with biomarker testing to reduce diagnostic gaps that currently prevent patients from accessing targeted therapies.
Curve Biosciences advances AI blood diagnostics
Curve Biosciences has developed AI-powered blood diagnostics that detect organ-specific disease signals by analyzing epigenetic markers in circulating DNA, validated in a 1,482-patient cirrhosis study. This approach shifts chronic disease monitoring from episodic snapshots to continuous molecular tracking of organ stress and aging progression.
Genomic Data: Why Sequencing Without Interpretation Stays Unused
Whole genome sequencing costs have dropped from $3 billion to a few hundred dollars, yet fewer than 1% of people have had their genome sequenced. The barrier is not price but interpretation—most people lack the framework to understand what genomic data means for their health or how to act on it.
Age-Associated Metabolites: AI Framework for Precision Monitoring
Immorta Bio and collaborators published a framework integrating multi-omics, artificial intelligence, and digital twins to operationalize precision medicine at scale. Analysis of metabolomic data from over 2,000 individuals identified nine age-associated metabolites, establishing a foundation for continuous, data-driven health monitoring and intervention.
AI’s role in the next era of longevity biotech
Artificial intelligence is compressing drug discovery timelines from four to five years to approximately fourteen months by automating target identification, molecular design, and clinical trial prediction. This acceleration has direct implications for longevity therapeutics, where speed to market can determine whether interventions reach patients before age-related decline progresses beyond intervention points.
Calcium-Free Arteries Don't Guarantee Cardiovascular Protection
A coronary calcium score of zero does not eliminate cardiovascular risk, particularly in individuals with sustained elevated LDL cholesterol. This finding challenges the assumption that absence of calcified plaque represents full protection against atherosclerotic disease progression.
ADDF launches new phase of $150m Alzheimer’s diagnostics accelerator
The Alzheimer's Drug Discovery Foundation has committed an additional $50 million to its Diagnostics Accelerator program, now totaling $150 million, to advance blood-based biomarkers, multi-marker panels, and AI-driven diagnostic tools for Alzheimer's disease. This investment targets earlier detection and disease monitoring to enable preventive interventions and combination therapies before irreversible neurodegeneration occurs.
AI Bridges Data Glut and Clinical Decision-Making
Reya.ai's acceptance into NVIDIA's Inception program signals the maturation of AI infrastructure designed to translate complex health data into actionable clinical decisions. The company addresses a fundamental constraint in longevity medicine: the gap between information abundance and interpretive capacity at the point of care.
The AI race to stop chronic disease just got funded with $3.5m
Dehaze, a Munich-based startup, has secured $3.5 million in funding to develop AI that processes the 97% of patient data currently unused in clinical decision-making, aiming to detect chronic disease earlier when intervention is more effective and less costly. Early detection of chronic conditions—currently missed in roughly one-third of cases until advanced stages—represents one of the largest opportunities for reducing healthcare spending and mortality.
Cell-Type Aging Signatures Predict Disease Years Early
Cell-type-specific aging signatures, measured through circulating proteins, predict disease onset including Alzheimer's, ALS, and lung cancer years before diagnosis. This shifts disease risk assessment from population-level genetics to individual cellular aging profiles, enabling earlier intervention.
AI Foundation Models Decode Multi-System Health Data
Human Longevity Inc. established Human Life Foundation Models to develop multimodal AI models for disease prediction and aging biology using de-identified clinical and multi-omic datasets. This infrastructure addresses a critical gap in personalized risk assessment and intervention discovery at scale.
A failed endpoint is not a failed technology
GRAIL's cancer screening trial missed its primary composite endpoint, but subgroup analysis reveals meaningful detection rates in specific cancer types, indicating the technology may have clinical value despite the headline failure. Understanding what the data actually shows versus what was reported requires interpreting signal-to-noise in early detection research.
Cardiac AI Marketplace Scales Early Detection Into Clinical Practice
HeartSciences launched an AI-ECG Algorithm Marketplace within its MyoVista Insights platform, enabling healthcare providers to access FDA-cleared cardiac AI tools through a single clinical system. This addresses a critical gap in medtech: translating validated algorithms into routine clinical practice at scale through a shared infrastructure model.
Digital cognition testing closes early detection gap in Alzheimer's
Plasma biomarkers can identify Alzheimer's pathology years before cognitive decline becomes clinically apparent, yet clinical assessment protocols lag far behind this detection capability. Digitized cognitive testing that targets vulnerable neural networks bridges this gap, enabling functional decline detection concurrent with biomarker identification—a prerequisite for meaningful early intervention.
Listening to biology’s early whispers
Chronic diseases develop through years of subtle, cumulative molecular drift before clinical diagnosis. The Buck Institute proposes longitudinal, AI-driven monitoring against individual baselines—treating each person as their own biological reference—to detect these early deviations rather than waiting for symptomatic presentation.
Social Inequality Accelerates Molecular Aging
Socioeconomic disparities correlate with accelerated epigenetic aging independent of chronological age, suggesting that structural inequalities directly influence the rate at which biological markers of aging progress. This finding implicates social determinants as primary drivers of aging trajectories, not merely correlates of health outcomes.
Vaginal aging biomarkers shift diagnosis from symptoms to measurable change
Vaginal aging involves measurable molecular changes that can be tracked through specific biomarkers, shifting the clinical understanding from subjective symptom reporting to objective biological assessment. This reframes reproductive aging as a systemic process with identifiable markers relevant to overall healthspan and quality of life.
Abu Dhabi’s “Future Health” initiative targets longevity, predictive care
Abu Dhabi is establishing a large-scale health infrastructure combining genomic, phenotypic, and wearable data with AI to enable predictive care and accelerate drug discovery. This represents a shift toward population-level early detection and prevention rather than reactive treatment models.
Single MRI Predicts Alzheimer's Cognitive Decline Trajectory
Researchers developed a machine learning framework that predicts Alzheimer's disease diagnosis, cognitive decline, and future trajectory from a single baseline MRI scan combined with demographic data. This approach enables earlier identification of cognitive decline patterns before symptoms fully manifest, shifting the timeline for potential intervention.
“Thinking” AI Outperforms Human Doctors on Real-Life Data
A reasoning-based large language model (o1-preview) outperformed human physicians on complex diagnostic reasoning tasks involving real clinical cases, achieving 78.3% accuracy in identifying correct diagnoses and 87.5% accuracy in recommending appropriate diagnostic tests. The performance gap widens on cases requiring synthesis of clinical information and justification of reasoning, suggesting AI systems can augment—or potentially exceed—human diagnostic capability in structured clinical decision-making.
AI-driven food app targets disease prevention and aging
Foodhak uses AI-driven personalization to translate nutrition science into daily food guidance aligned with individual health markers and goals. The approach positions food as preventive medicine rather than caloric constraint, addressing a systemic gap where most food choices optimize for taste and cost rather than long-term health outcomes.
The Immune‐Autonomic Interface in Aging: Baseline Immune Profile Shapes Cardiac Autonomic Response to Exercise
Baseline immune cell profiles in older adults predict how their heart rate variability responds to acute exercise stress. This immune-autonomic relationship reveals why individuals show heterogeneous physiological resilience during aging, informing personalized intervention strategies.
Cognitive toolkit detects early Alzheimer’s signs
Researchers developed a Mandarin-language cognitive toolkit to detect early Alzheimer's signs in older Chinese Americans, addressing diagnostic gaps created by language and cultural bias in English-based assessments. The validated tests correlate with English measures and early blood-based biomarkers, enabling earlier intervention and broader research participation in underserved communities.
Liver Aging Index Outperforms Age in Predicting Mortality
Researchers developed the Liver Aging Index (LAI), a noninvasive biomarker combining clinical measurements, plasma markers, and imaging data to assess biological liver aging independent of chronological age. The LAI predicted all-cause mortality and liver-related events more accurately than age alone across three large independent cohorts, with accelerated liver aging conferring 22–85% higher mortality risk per standard deviation increase.