Aug 2026· Personalized Medicine· pp.
1-16
· 0 citations· 102 references
Medicine
TL;DR
This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies.
Abstract
Precision medicine unifies the latest capabilities from engineering, biotechnology, and artificial intelligence (AI) to deliver data-driven personalized healthcare. This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies. Relevant peer-reviewed studies were identified in a systematic search of PubMed, Scopus, Web of Science, and Google Scholar for any articles published during the period of 1 January 2015 through 31 December 2024. Advanced biosensors provide immediate feedback regarding the molecular and physiological status of patients, allowing for the rapid definition of new biomarkers, as well as ongoing evaluations of their clinical status and response to treatments. Predictive AI models enhance the precision of patient stratification, treatment planning through adaptive therapies, and predicting individual drug responses, while also lessening the risk of adverse events from treatments. Several emerging technologies illustrate a demonstrated movement toward proactive precision medicine, such as pharmacovigilance based on smart biosensors and closed-loop delivery systems. Key barriers still exist, such as the need for interoperable standards, scientific validation of tools, legal applicability, and ethical issues; therefore, resolving these issues requires collaborative interdisciplinary teams conducting long-term clinical studies.
Pharmacogenomics and artificial intelligence (AI) are emerging as important drivers of precision medicine, enabling healthcare systems to adopt individualized therapeutic approaches. Pharmacogenomics examines how genetic variations influence drug response, efficacy, metabolism, and toxicity, while AI provides advanced computational tools for analyzing complex genomic and clinical data. This review highlights the integration of AI-driven pharmacogenomics in personalized therapy and its potential to improve treatment outcomes. Machine learning, deep learning, natural language processing, and big data analytics are increasingly used to identify genetic variants, predict drug responses, optimize medication selection and dosage, and reduce adverse drug reactions. These technologies support the interpretation of large-scale genomic information and facilitate evidence-based clinical decision-making. Significant applications have been demonstrated in oncology, cardiovascular diseases, neurological and psychiatric disorders, and rare genetic diseases, where personalized treatments can enhance therapeutic efficacy and patient safety. Recent advances in genomic sequencing, multi-omics integration, digital health technologies, explainable AI, and real-time patient monitoring have further expanded the scope of precision medicine. However, challenges related to data privacy, algorithm bias, regulatory frameworks, and clinical implementation remain. Future developments in explainable AI, predictive analytics, and AI-powered personalized therapy are expected to improve treatment precision and accelerate the realization of truly individualized healthcare. Overall, the convergence of AI and pharmacogenomics represents a transformative approach to modern medicine with substantial potential to improve patient outcomes and optimize therapeutic interventions.
Kabhi Khanna, Chetna Chhabra, Rohit Saroha et al.· Emerging Trends in Personali...· 0 citations
Artificial intelligence (AI) has transformed the modern medical landscape. Medical devices benefit from innovative data collection, utilization of machine learning, predictive analytics, and intelligent decision support systems in their design and implementation. Applications include medical devices for diagnosis, screening, patient monitoring, tailored care, robotic procedures, and streamlining hospital processes. Despite these benefits, the development of new AI-powered devices presents many new regulatory, legal, ethical and cyber security challenges to be addressed. This research paper provides a review of the recent literature regarding advances in AI-powered medical devices, including their mechanisms of action, clinical applications, regulation, and regulation challenges. A qualitative narrative review was performed synthesizing the content of 15 carefully selected papers that fit the inclusion criteria. An analysis revealed that AI-powered medical devices have improved accuracy in diagnosis, enabled precision care, advanced patient monitoring, and optimized component manufacturing. Our review further identified a future where evolving regulation frameworks, such as risk-based control strategies, as well as increased explainability, interpretability, infrastructure protection, and compliance will help to facilitate widespread AI adoption in medicine.
C. Liakou, Marios Papadakis, Markos Plytas· International Journal of Med...· 0 citations
Precision medicine offers the opportunity to improve the benefit–risk profile of new therapies by prospectively identifying patients most likely to respond or least likely to experience harm; however, its systematic integration into drug development remains inconsistent outside oncology. Key barriers include timely generation of robust predictive biomarker hypotheses (i.e., hypotheses regarding treatment‐by‐biomarker interactions), appropriate validation strategies, and coordinated development of companion diagnostics within increasingly complex FDA, European Medicines Agency, and In Vitro Diagnostic Regulation regulatory frameworks. This review presents a pragmatic operational framework to guide the incorporation of precision medicine into drug development across therapeutic areas. We outline structured approaches for early biomarker hypothesis generation and prephase II evidence development; define five clinical development scenarios based on the strength of the biomarker signal—front‐loaded enrichment, precision‐medicine‐enabled phase II, adaptive phase II, adaptive phase III, and back‐loaded confirmatory enrichment; and summarize regulatory and lifecycle considerations for companion diagnostics. Examples from oncology and emerging applications in cardiovascular and metabolic diseases illustrate evolving regulatory expectations and common industry challenges. We also discuss opportunities to extend precision medicine beyond predictive biomarkers to diagnostic, prognostic, and safety biomarkers, as well as digitally derived signatures. Treating precision medicine as a core component of drug development—rather than an optional enhancement—can improve clinical trial efficiency, support commercial viability, and ensure that patients derive meaningful clinical benefit. Early, structured biomarker planning; integrated clinical–diagnostic strategies; and iterative collaboration among sponsors, regulators, HTA bodies, payers, and patients are critical for translating biomarker insights into de‐risked pivotal trials and aligned regulatory and market‐access decisions.
Ingrid Holst-Laubjerg, J. Moreira· Clinical pharmacology and th...· 0 citations
Emerging biomarkers are fundamental for the transition to precision medicine in IBD, aiming to enhance pathogenesis understanding, personalize therapies, and improve patient quality of life, establishing pathways for more effective, individualized management approaches.
Matheus Querino da Silva, João Daniel de Souza Menezes, José Luis Esteves Francisco et al.· PLoS ONE· 0 citations
The integration of digital therapeutics (DTx), wearable electronic devices, and artificial intelligence (AI) represents a significant advancement in personalized healthcare. The primary purpose of this structured narrative review is to evaluate the convergence of these technologies, providing a consolidated framework that bridges the gap between raw biometric data acquisition and actionable, AI-driven clinical insights. This paper synthesizes the latest literature on the intersection of mobile health (mHealth), machine learning (ML), and physiological tracking, with a primary focus on heart rate variability (HRV) and associated biochemical markers, such as cortisol, salivary alpha-amylase, and interleukins. Instead of viewing wearable outputs simply as raw data, we critically evaluate the technical verification and clinical validation required to define them as true “digital biomarkers.” By evaluating multimodal sensor technologies and advanced predictive algorithms, this paper outlines the clinical utility of digital biomarkers in diagnosing and proactively managing cardiovascular, neurological, metabolic, and psychiatric conditions, noting classification accuracies frequently exceeding 85% in controlled settings. However, we strongly caution that internally validated performance in controlled settings does not inherently demonstrate external clinical utility. The clinical relevance of this study lies in its holistic approach to identifying how continuous monitoring can broaden healthcare accessibility while improving precision medicine. Furthermore, it deeply addresses the technical challenges of highly variable ambulatory data quality, the necessity for robust artifact reduction (e.g., via LSTM and GAN architectures), and the limitations of small, homogeneous training datasets. We highlight the essential need for demographic-aware algorithmic models, external validation, and decentralized privacy-preserving models (e.g., federated learning) in diverse populations to ensure the safe, equitable clinical translation of DTx, mHealth, ML, and AI technologies.
Kwanjoon Park, Eunice Kwan Chae Park, W. Park et al.· Bioengineering· 0 citations
Vascular aging is a fundamental contributor to the development of chronic diseases and has emerged as a critical focus in biomedical research. With the rapid advancement of artificial intelligence (AI), new opportunities have arisen to enhance the precision and efficiency of vascular aging studies. AI techniques, particularly those applied to large-scale multi-omics and medical imaging data, enable the identification of novel biomarkers and the development of robust models to quantify the rate and extent of vascular aging. For example, AI has been successfully deployed for multi-omics biomarker discovery, automated quantification of coronary plaque and calcium scoring from CT imaging, and the development of vascular aging clocks based on retinal images or photoplethysmography (PPG). These approaches facilitate early detection, individualized risk assessment, and potential intervention strategies. This review provides a comprehensive overview of current AI applications in vascular aging, spanning from basic mechanistic research to clinical risk prediction. It also discusses future directions and key challenges, including the need for external validation, algorithmic fairness, domain adaptation, and model interpretability, emphasizing the transformative role of AI in advancing precision medicine for vascular health.
Zhongling Dai, Chenggong Ma, Yuke Chen et al.· Ageing Research Reviews· 0 citations