This commentary highlights the age illusion and advocates integrating biological age and artificial intelligence (AI) to enhance clinical precision in health care. By synthesizing geroscience and machine learning, practitioners and scientists can effectively bridge the gap between experimental biomarkers and personalized medicine and longevity care. This contribution is timely as the importance of biological age and predictive analytics offer transformative potential for diagnostics while necessitating a focus on modern medical systems. This article aims to elucidate how AI accelerates the clinical implementation of biological age rather than current chronological age in precision medicine.

Biological age estimations

The clinical overreliance on chronological age often fails to capture the significant physiological variability among individuals and requires further elucidation. Importantly, emerging causality-informed epigenetic clocks, such as DamAge, distinguish accumulated molecular damage from adaptive responses, offering superior predictive power for morbidity compared with chronological metrics. Furthermore, recent plasma protein-based models demonstrate that organs age at distinct rates, providing a granular view of system-specific vulnerabilities to diseases such as heart failure and dementia. To integrate these findings into clinical practice, we propose implementing multi-omic frameworks that integrate proteomics and DNA methylation within standardized platforms like Biolearn to guide high-stakes decisions, such as surgery eligibility and intensive care triage. This comment piece advocates training clinicians to use longitudinal biological age profiling to identify accelerated functional decline in younger adults. Utilizing transparent, organ-specific aging models to recalibrate screening thresholds currently dominated by chronological age (CA). The challenge ahead is not to discard age entirely, but to contextualize it within a more precise and equitable model of care. The transition from chronological age to biological age (BA) estimations represents a paradigm shift in longevity medicine, moving away from what has been termed the “age illusion” toward a precision-based understanding of physiological reserve. While CA remains the standard for clinical triage and screening, it is an increasingly imprecise proxy for individual health status, often failing to account for the significant heterogeneity in aging trajectories among individuals of the same calendar age1. A critical advancement in BA estimation is the development of “causality-informed” epigenetic clocks. Unlike first-generation clocks that were purely correlative, newer models like DamAge distinguish between accumulated molecular damage and protective, adaptive responses. This distinction is vital for longevity medicine; a metric that only measures age-related changes without identifying their causal impact on mortality risk provides limited actionable data for therapeutic intervention. By filtering for CpG sites that directly contribute to functional decline, these clocks offer a more robust framework for evaluating the efficacy of geroprotective treatments.

However, the field is evolving beyond global BA toward organ-specific aging models. Recent research utilizing plasma protein-based signatures has demonstrated that organs age at distinct rates. These proteomic models have unveiled that accelerated aging of specific organismal systems—such as the cardiovascular or central nervous systems—can predict the onset of diseases like heart failure or dementia years before clinical symptoms manifest. This approach allows for “healthy longevity medicine” to shift from reactive treatments to proactive, system-specific preservation.

The integration of these biomarkers into clinical practice is further supported by the rise of longevity biotechnology and artificial intelligence(AI)-driven platforms like Biolearn, which aim to standardize the interpretation of diverse biological datasets. Yet, significant challenges remain. The medical community must address the “limitations of chronologic age” by recalibrating screening thresholds and clinical guidelines to reflect biological reality. This requires not only technological innovation but also a shift in medical education and ethics to ensure that BA-based decisions do not inadvertently lead to new forms of clinical inequity. While BA estimations provide a sophisticated lens through which to view human aging, their clinical utility depends on the continued refinement of organ-specific proteomic signatures and the validation of causal epigenetic markers. Moving forward, the synthesis of geroscience, artificial intelligence, and clinical applications will be essential to bridge the gap between experimental biomarkers and evidence-based longevity geriatric care.

Artificial intelligence use for biological age estimations and health care
Notably, artificial intelligence is being developed and used for medical treatments, drug development, and robotic surgery, enhancing the effectiveness and safety of healthcare. AI methods, including deep learning, graph neural networks, transformer-based architectures, and multimodal foundation models, are increasingly enabling the integration of longitudinal clinical data with multi-omic, imaging, wearable sensor, and environmental datasets to generate more accurate estimates of biological age and predict trajectories of healthy aging. By leveraging repeated measurements over time rather than single cross-sectional assessments, these approaches facilitate continuous monitoring of biological aging, early identification of accelerated aging, and dynamic evaluation of responses to lifestyle modifications and geroprotective interventions. In clinical practice, AI-driven biological age models have the potential to provide decision support by improving individualized risk stratification, guiding preventive and therapeutic interventions, and advancing precision longevity medicine through personalized management strategies that account for age-, sex-, and disease-specific characteristics. Moreover, AI and blockchain technologies have rapidly gained traction in many academic and professional domains. AI-driven technologies in remote healthcare enhance patient care through improved diagnostics, monitoring, and medical treatment. AI applications such as AI-enabled diagnostic systems, predictive analytics, and teleconsultation platforms overcome the limitations of traditional remote health care models9. The above-mentioned use of machine learning algorithms enables the analysis of vast amounts of patient data to predict health complications and tailor care plans10. AI is also changing how patients manage their medical conditions, particularly chronic diseases, through self-care tools, such as chatbots and health monitoring technologies. AI-enabled virtual assistants can provide medication reminders and deliver health advice to older individuals, empowering patients and improving outcomes.

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