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Review Article | DOI: https://doi.org/10.31579/2641-0419/462
1Cedars Sinai Medical Center, Los Angeles, CA.
2Stem Cell & Anti-Aging Institute, Beverly Hills, CA, USA.
3Euclid University, Washington DC.
*Corresponding Author: Ernst von Schwarz Schwarz, Stem Cell & Anti-Aging Institute 324 S. Beverly Drive, #711, Beverly Hills, CA 90212, USA.
Citation: Ernst R. von Schwarz, Julian L. Bruce, Laurent Cleenewerck de Kiev, (2025), Algorithms to Ageless: AI in Anti-Aging Medicine t, J Clinical Cardiology and Cardiovascular Interventions, 8(5); DOI: 10.31579/2641-0419/462
Copyright: © 2025, Ernst von Schwarz Schwarz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 13 March 2025 | Accepted: 28 April 2025 | Published: 15 April 2025
Keywords: aging research; anti-aging; artificial intelligence (ai); biological aging clocks; ai-assisted drug discover
The adoption of artificial intelligence (AI) in medicine in general and in scientific anti-aging efforts in particular represents a new era of research, diagnostics, and therapy. We here review and discuss the AI-driven methodologies that accelerate the identification of therapeutic targets, predictive biomarkers, and precision-driven interventions, as well as recent breakthroughs, including the application of deep learning (DL) algorithms to screen large chemical libraries, leading to the discovery of senolytic compounds that selectively eliminate aging cells. AI-powered biological aging clocks, trained on genomic, proteomic, metabolomic, and epigenomic data, enable accurate predictions of biological age that may help to optimize early intervention strategies for age-related diseases. AI-based bioinformatics platforms have identified novel compounds that enhance collagen synthesis and mitigate oxidative stress, thus offering new avenues for personalized skincare and anti-aging therapeutics.
Issues for clinical application remain, including ethical concerns and a lack of robust validation frameworks, interdisciplinary collaboration, and appropriate education on AI methodologies. Given that aging-related interventions primarily target middle-aged to elderly populations, clinicians must be equipped to respond to patient concerns and communicate AI-generated insights effectively, fostering trust in AI-assisted anti-aging medicine.
The utilization of artificial intelligence (AI) has recently entered anti-aging research and therapy. AI-driven methodologies have impacted the discovery of therapeutic interventions targeting the aging process, resulting in advances in drug discovery, biological aging clocks, and a personalized approach to longevity medicine. The American Medical Association (AMA) defines AI's role in healthcare as "augmented intelligence," emphasizing its ability to enhance human decision-making rather than replacing it. Given medicine's innate complexity and unpredictability, the AMA describes AI as a "partnership between man and machine," expanding the boundaries of what healthcare professionals can achieve [1].
AI was successfully used in medicine in the 1970s with MYCIN, an early AI algorithm developed to support and simplify the detection of specific bacterial infections [2]. DXplain, a decision support system developed in 1980, provided differential diagnoses and disease descriptions, gradually expanding its database to over 2,400 conditions [3]. Despite these early applications, limitations in computing power and algorithmic sophistication prevented widespread adoption. However, with advancements in DL and computational modeling in the 2000s, AI's role in medicine evolved, gaining more traction as an indispensable tool in clinical decision-making [4].
AI's impact on anti-aging research is evident in its acceleration of multi-omics data analysis, high-resolution imaging, and predictive modeling [5]. AI-powered aging clocks leverage genomic, epigenomic, proteomic, and metabolomic data to refine biological age predictions, enabling early detection of age-related diseases and developing personalized intervention strategies [6]. Similarly, AI-assisted drug discovery platforms can identify senolytic compounds that focus on cellular senescence, potentially directing targeted anti-aging strategies [7].
AI-driven imaging technologies, such as 3D Line-Field Confocal Optical Coherence Tomography (LC-OCT) and DL-based cellular analysis, augment the dermatologic armamentarium by detecting age-related skin changes at unprecedented resolution, beyond the capacity of the human eye and expertise [8]. Additionally, AI-guided regenerative medicine strategies, including single-cell transcriptomics and machine-learning-driven cellular reprogramming, may offer novel therapeutic tools for longevity interventions [9].
Despite these advancements, numerous challenges remain. AI's clinical adoption is complicated by data standardization issues, algorithmic transparency, and ethical concerns related to patient privacy, bias, and informed consent, among other bioethical concerns. Moreover, the field requires greater interdisciplinary collaboration between AI researchers, biomedical scientists, and clinicians to ensure that AI-driven insights are scientifically rigorous and clinically actionable [5].
Aging
Aging is a natural, continuous process in living organisms that results in declining internal and external functional capacity, persistence, and vitality. Depending on genetic, cellular, and environmental factors, it affects humans at different rates. While aging is a fundamental part of life, research focuses on ways to reverse age-related degeneration of different organ systems, including the skin, blood vessels, heart, and brain tissue [13].
Factors leading to or causing aging include oxidative stress, the accumulation of free radicals, metabolic garbage, shortening of telomeres, vascular stiffness, lack of repair mechanisms, oxygen and energy deprivation, mitochondrial damage, and loss of stem cells in number and quality. These elements ultimately lead to the loss of cellular membrane integrity, cellular damage, and subsequent death.
Increased levels of ROS lead to cellular senescence, a physiological mechanism that prevents cellular proliferation in response to damage incurred during replication. Senescent cells accumulate with age, leading to age-related skin changes and pathologies. The Hayflick limit describes the inability of telomeres to maintain their lengths due to the replication process, causing cells to lose their ability to proliferate and enter a stage of irreversible cell cycle arrest, termed cellular senescence [13].
Senescent cells resist apoptosis and secrete factors that promote inflammation and DNA damage. These cells contribute to tumorigenesis and various age-related malignancies due to the secretion of the senescence-associated secretory phenotype (SASP). Senescent cells are characterized by a persistent DNA damage response, which can also be induced by ionizing radiation, chemotherapeutics, genotoxic stress, and oxidative stress [13]. With the accumulation of senescent cells contributing to age-related diseases, senolytic drugs have proven therapeutic potential in treating these disorders. Senolytic drugs can extend lifespan and delay age-related physical decline in normal mice, suggesting their effectiveness in age-related diseases [14].
AI Utilization in Anti-Aging Research
AI is becoming a useful methodology in anti-aging medicine by integrating non-invasive imaging technologies, synthetic biology, and machine learning (ML) to develop diagnostic tools and therapeutic strategies [15]. AI-assisted techniques currently enhance skin imaging, drug discovery, and cellular-level aging analysis, supporting goal-directed therapies and their efficacy measures [16]. For example, AI imaging techniques can determine collagen content and improvements in circulation to evaluate the effects of anti-aging skin treatments (reference needed).
One of the most promising AI applications in dermatology and aging research is high-resolution skin imaging, used for diagnosis, treatment monitoring, and surgical planning. AI-enhanced Confocal Laser Scanning Microscopy (CLSM) and Multiphoton Laser Scanning Microscopy (MPLSM) allow for detailed visualization of micrometric features within superficial skin layers. Recent advancements, such as 3D Line-Field Confocal Optical Coherence Tomography (LC-OCT), enable ultra-high-resolution imaging of skin histology and cellular structures, significantly improving the ability to assess age-related morphological changes [17].
AI-driven quantitative cellular analysis is increasingly significant in refining aging diagnostics. High-resolution images obtained through LC-OCT and other imaging modalities undergo AI-assisted analysis, enabling measurements of cellular characteristics, including nuclei size, shape, and network atypia. A recent study collected imaging data from three distinct anatomical sites: the upper face at the temple, the central face at the malar region, and the lower jawline at the inferior jaw. The AI analysis identified age-associated changes, such as slight stratum corneum thickening, increased nuclear count, a less dense cellular network, and greater nuclear heterogeneity [18]. These findings demonstrate AI’s capability to quantify and monitor aging at the cellular level, enhancing early detection methods and improving the precision of targeted interventions.
Beyond imaging, AI plays a role in discovering and developing senolytic anti-aging drugs. Combining synthetic biology with ML can identify novel therapeutic agents targeting aging-related processes, such as fibrosis, inflammation, cellular damage, and cancer progression. AI-assisted drug screening platforms can analyze vast chemical libraries and predict compound efficacy and toxicity, significantly accelerating the identification of promising anti-aging therapies [19].
AI Algorithms for Senolytic Drug Development
Senolytic drugs aim to eliminate senescent cells associated with age-related degeneration, such as cardiovascular disorders, neurodegenerative diseases, and possibly cancer [20]. A group at the University of Edinburgh developed a machine learning model to discover new senolytic drugs. The XGBoost AI algorithm recognizes features of chemicals with senolytic activity, using data from more than 2,500 chemical structures for model training. The AI screening identified 21 potential drug candidates for experimental testing. Tests in human cells revealed that three compounds, ginkgetin, periplocin, and oleandrin, could eliminate senescent cells while preserving the integrity of healthy cells. Interestingly, these compounds are naturally derived from traditional herbal medicines, with oleandrin being the most effective [21].
MIT and the Wyss Institute researchers utilized graph neural networks to screen 2,352 compounds for senolytic activity in a model of etoposide-induced senescence. This AI-guided method revealed three highly selective and potent senolytic compounds from a chemical space of over 800,000 molecules. The compounds displayed chemical properties suggestive of high oral bioavailability and favorable toxicity profiles in hemolysis and genotoxicity tests. Structural and biochemical analyses indicated that these compounds bind Bcl-2, a protein that regulates apoptosis and is also a chemotherapy target. Molecular docking simulations and time-resolved fluorescence energy transfer experiments confirmed the binding affinity of these compounds to Bcl-2, highlighting their potential as effective senolytics. Furthermore, in vivo testing in aged mice demonstrated a significant reduction in senescent cell burden and decreased expression of senescence-associated genes in the kidneys [22].
Others confirmed that ouabain, a drug traditionally used to treat heart conditions, has potential senolytic properties due to its ability to induce apoptosis in senescent cells while sparing healthy cells selectively. This effect is mediated by inhibiting the Na, K-ATPase pump, which activates signaling pathways involving Src, p38, Akt, and Erk2. These pathways are crucial for the survival of senescent cells, and their inhibition by ouabain triggers cell death. Ouabain's senolytic activity is enhanced by ATP1A1 knockdown and can be mitigated by supplemental potassium. This discovery highlights the potential of repurposing existing drugs for senolytic applications, offering a promising avenue for developing new anti-aging therapies [23].
AI to Promote Healthy Skin
AI has emerged as a tool increasingly used in dermatology and oncology, significantly enhancing skin health assessment, cancer detection, and precision treatment strategies [24]. AI’s ability to analyze high-resolution dermatological images, integrate multi-omic datasets, and predict treatment responses has established it as a cornerstone in modern dermatologic and oncologic research [25].
DL models, particularly convolutional neural networks (CNNs), have enabled accurate classification of skin lesions. A landmark study by Esteva et al. (2021) employed a deep CNN trained on over 129,000 clinical images to distinguish between benign and malignant skin conditions. The AI model achieved a diagnostic accuracy comparable to board-certified dermatologists, outperforming general practitioners in melanoma detection with an AUC (area under the curve) of 91.6% [26].
Tschandl et al. (2023) examined the efficacy of AI in detecting skin cancers (NMSC) and melanoma using dermoscopic images from 15,000 patients. The authors found that AI-assisted diagnostic workflows improved clinician performance by reducing false negatives and increasing early detection rates. The AI model demonstrated a diagnostic sensitivity of 92.4% and specificity of 89.1%, outperforming independent human dermatologists in large-scale validation trials [27].
Moreover, spectral AI imaging technologies have been integrated into non-invasive diagnostic tools such as reflectance confocal microscopy (RCM) and multiphoton tomography (MPT). These techniques use AI-enhanced feature recognition to detect pre-malignant and malignant lesions with superior accuracy. Studies have demonstrated that AI-powered RCM can distinguish basal cell carcinoma from benign skin conditions with 95
Applications of AI in Anti-Aging Medicine
Advances in generative modeling, multimodal AI integration, and dynamic biological aging clocks will shape the future of AI in anti-aging research. Generative Adversarial Networks (GANs) and Large Language Models (LLMs) trained on vast biomedical datasets will continue to drive novel protein-ligand interaction predictions, drug repurposing strategies, and synthetic biological modeling. Recent studies have shown that GANs can generate synthetic transcriptomic and proteomic profiles, allowing researchers to explore hypothetical aging interventions without requiring large-scale human trials [31]. Multi-omics-driven AI models will provide deeper insights into the interplay between genetics, epigenetics, metabolism, and protein function in aging. AI-powered integrative aging clocks, capable of analyzing genomic, proteomic, metabolomic, and transcriptomic data, will improve the accuracy of biological age estimation and risk stratification for age-related diseases. Combining longitudinal health data with AI-driven predictions will enable real-time monitoring of biological aging, allowing for highly individualized longevity strategies [5].
Beyond discovery and modeling, AI is expected to enhance regenerative medicine and cellular reprogramming technologies. Recent breakthroughs in AI-driven single-cell transcriptomics have allowed for identifying cell fate transitions during aging, providing the foundation for rejuvenation therapies and stem cell-based interventions. These AI-driven insights could pave the way for therapies that reverse cellular senescence, improve tissue regeneration, and extend health span [48].
Challenges and Limitations of AI in Anti-Aging Medicine
Despite advances in AI-driven anti-aging research, several scientific and technical challenges persist. Data quality and standardization are among the most pressing issues, threatening the accuracy and proper outputs of even the most robust models. Aging research generates vast, heterogeneous datasets that vary in quality, format, and completeness. AI models require large, high-quality, well-annotated datasets to make reliable predictions. However, inconsistencies in multi-omics data collection, imaging modalities, and clinical parameters can introduce biases or lead to misleading conclusions. Ensuring standardized data pipelines and developing federated learning approaches that enable secure data sharing without compromising privacy are critical to improving AI model reliability [5].
Another challenge is the lack of AI explainability and interpretability. Many deep learning models operate as "black boxes," producing highly accurate predictions without providing insight into the underlying biological mechanisms of aging. This opacity limits their clinical adoption, as physicians and researchers may hesitate to trust models they cannot fully understand. Developing XAI frameworks, which offer transparency into how AI models generate predictions, is essential for increasing clinician confidence and facilitating regulatory approval of AI-driven aging interventions [42].
Ethical, regulatory, and privacy concerns also pose significant risks. AI applications in anti-aging medicine involve analyzing sensitive personal health data and raising issues related to data security, informed consent, and algorithmic bias. Regulatory frameworks for AI-driven longevity interventions are still evolving, and transparency in AI decision-making is crucial for public trust. Ethical guidelines must ensure that AI-driven longevity medicine balances innovation with patient safety, equitable access to interventions, and fair treatment across diverse populations [49]
Education on AI Technologies
AI models must be co-developed with physicians, biomedical researchers, and ethicists to ensure clinical relevance and real-world applicability. Interdisciplinary collaboration between AI engineers and healthcare professionals can facilitate the creation of user-friendly and transparent AI systems, essential for widespread adoption in aging medicine. AI-generated recommendations must be interpretable and contextualized within established medical knowledge so physicians can effectively communicate insights to their patients and make informed clinical decisions. Given that patients most interested in anti-aging therapies are often middle-aged to elderly and are well-knowledgeable about their health condition, healthcare professionals must be able to explain what AI can provide and how [50]. Currently, this might be challenging since clinicians lack any formal appropriate training in AI methodologies, requiring hands-on training in AI technology and methods, model validation techniques, and the critical evaluation of AI-generated predictions [51].
As AI becomes increasingly utilized in conjunction with personalized anti-aging interventions, clinicians must be equipped with effective communication strategies to explain AI-driven longevity assessments in clear and accessible terms. This includes interpreting predictive models, discussing uncertainties, and setting realistic expectations for AI-enhanced interventions. Training programs should prepare healthcare providers to navigate AI-generated longevity strategies' ethical and clinical implications, ensuring patients receive accurate, balanced, and actionable information [52, 53].
Strategies and Recommendations for Advancing AI Integration
We need a multi-faceted approach involving technology, collaboration, and regulation to tackle the challenges in AI-driven anti-aging medicine. Institutions should use standardized methods for collecting and integrating multi-omics data to improve quality and consistency. Federated learning and decentralized data systems can support collaborative research while protecting patient privacy and adhering to data laws [5]. Developing XAI tools is crucial for transparency, helping clinicians understand and trust AI predictions. Open-source frameworks and clear documentation will enhance reproducibility and peer review, ensuring clinical usefulness across different populations [41, 42].
Long-term solutions require changes in education and regulation. Adding AI training to medical education and ongoing training programs can bridge the gap between clinicians and data scientists. Interdisciplinary teams, including ethicists, computer scientists, and healthcare professionals, should co-develop AI tools to ensure they are clinically relevant and ethically sound [51-53]. Global regulatory bodies need to create unified standards for approving, auditing, and monitoring AI-based anti-aging treatments to ensure safety, fairness, and accountability. These coordinated efforts are essential for AI to promote health and longevity effectively [49].
AI is revolutionizing anti-aging research and therapy, offering unprecedented insights into aging mechanisms, therapeutic discovery, and precision-driven and personalized longevity interventions. The application of AI-driven advancements such as senolytic compound discovery, biological aging clocks, and personalized longevity medicine augments early disease detection and extending healthspan. Data standardization, AI model explainability, and ethical concerns regarding privacy and bias must be addressed for broader clinical adoption. Clinician education and interdisciplinary collaboration are crucial to integrate AI into anti-aging medicine.
Key Points:
Issues Relating to Journal Policies: We have carefully reviewed the Clinical Cardiology and Cardiovascular Interventions policies and guidelines and have ensured that this manuscript complies with all submission requirements. There are no issues relating to journal policies that need to be addressed
Declaration of Potential Competing Interests: No conflicts of interest exist by any of the authors.
Author Approval and Confirmation of Originality:We confirm that all authors approve the submission of this manuscript to the Clinical Cardiology and Cardiovascular Interventions. Additionally, we affirm that the content of this manuscript has not been published or submitted for publication elsewhere.
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