Personalized healthcare is no longer just a promise of precision medicine — this review shows where it is already becoming clinically real, from AI prediction and pharmacogenomics to wearable monitoring and tailored care pathways. Across more than 1,300 original studies, the paper maps which personalized strategies are truly improving diagnosis, chronic disease control, and care delivery — and where the field still risks failing on fairness, infrastructure, and real-world implementation.
Additional notes
References [347] and [1147] are cited in preprint form.
Abstract: The aim of this paper is to synthesize evidence regarding the development, implementation, and evaluation of personalized healthcare strategies, focusing on AI-driven predictive modeling, digital health technologies, and the socio-educational barriers to clinical integration. The review utilises 1334 original studies with 9573007 total participants (topic deduplicated ΣN). This evidence map suggests that personalized healthcare is increasingly being operationalized through artificial intelligence-enabled prediction, pharmacogenomics, digital self-management, and continuous sensing, with prominent signals including a 10.70% gain in diagnosis prediction, up to 97.16% diagnostic accuracy in personalized federated smart healthcare, and clinically relevant improvements in chronic disease management such as better glycemic and blood pressure control. Across the mapped literature, the most consistent pattern was that individualized recommendations, monitoring, and communication were associated with better alignment of care to patient risk, treatment response, and daily context, particularly in diabetes, cardiovascular care, medication management, rehabilitation, and maternal or elderly care. The review also highlights that personalized healthcare is not only a computational enterprise but a delivery-system challenge, because implementation depends on interoperable data infrastructure, privacy-preserving governance, workforce readiness, and equitable representation in genomic and algorithmic models. For practice, the findings support a role for embedding personalized decision support, remote monitoring, and tailored education into routine care pathways where they can strengthen prevention, adherence, and earlier intervention. At the same time, the mapped evidence remains heterogeneous and often short term, so the field would benefit most from prospective, clinically embedded studies that use standardized outcomes, include diverse populations, and test whether personalized models remain effective, fair, and sustainable over time.
Keywords: Personalized Healthcare; Federated Learning; Electronic Health Records; Machine Learning; Wearable Health Monitoring; Patient-Centered Care; Predictive Analytics; Deep Learning; Precision Medicine; Health Information Technology
Review Stats
Final search date and database lock: 2026-04-11 23:38:06 CEST
Plan: Pro (expanded craft tokens; source: PubMed)
Source: PubMed
Total Abstracts/Papers: 3384
Downloaded Abstracts/Papers: 3384
Included original and non-original Abstracts/Papers (all): 1752
Included original Abstracts/Papers (Vote counting by direction of effect): 1334
Reference Index (links used in paper): 337
Total participants (topic deduplicated ΣN): 9573007
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[1292] Impact of Precision-Guided Dosing on Clinical Decision-Making and Health Care Utilization in Inflammatory Bowel Disease: A Retrospective Pretest/Posttest Real-World Study. — https://doi.org/10.1093/crocol/otaf044
[1298] The Importance of Implementing a Transition Strategy for Patients with Muscular Dystrophy: From Child to Adult-Insights from a Tertiary Centre for Rare Neurological Diseases. — https://doi.org/10.3390/children10060959
[1313] Association between burnout and PTSD, and perceived stress in the workplace among healthcare workers in the intensive care unit: a PsyCOVID-ICU substudy. — https://doi.org/10.1080/13548506.2025.2454038
[1333] Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship. — https://doi.org/10.1371/journal.pdig.0000641
[1375] Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach. — https://doi.org/10.1093/jamia/ocae327
[1384] Metabolomic Profiling Identifies Early Biomarkers of Frailty, Balance Impairment, and Fall Risks in Older Adults. — https://doi.org/10.1159/000546772
[1386] Guided paths to understanding: can patient navigation improve health literacy among adults in the Philippines? — https://doi.org/10.1093/heapro/daae207
[1394] Harnessing Clinical and Biochemical Data for Personalized Cardiovascular Risk Prediction: a Machine Learning Approach Toward Precision Nutrition. — https://doi.org/10.1016/j.tjnut.2026.101363
[1446] Usability and Usefulness of SMS-Based Artificial Intelligence Intervention (Mwana) on Breastfeeding Outcomes in Lagos, Nigeria: Pilot App Development Study. — https://doi.org/10.2196/65157
[1448] Perspectives on eye care access and telemedicine-based glaucoma screening among Latine individuals with limited English proficiency. — https://doi.org/10.1016/j.ajoint.2025.100144
[1450] Integrated bio-cooperative robotic platform for virtual cognitive training in Parkinson's disease: design and methodology of the OPERA project. — https://doi.org/10.3389/fneur.2025.1680215
[1472] Does Food Habits and Malnutrition Affect Health Perception Among Diabetic Patients? A Mediation and Moderation Analysis. — https://doi.org/10.3390/healthcare13101093
[1480] Development, Implementation and Assessment of Molecular Diagnostics by Next Generation Sequencing in Personalized Treatment of Cancer: Experience of a Public Reference Healthcare Hospital. — https://doi.org/10.3390/cancers11081196
[1491] Next-Day Medical Activities Recommendation Model with Double Attention Mechanism Using Generative Adversarial Network. — https://doi.org/10.1155/2022/6334435
[1494] Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning. — https://doi.org/10.34133/2022/9892340
[1620] Effectiveness of weekly cell phone counselling calls and daily text messages to improve breastfeeding indicators. — https://doi.org/10.1186/s12887-018-1308-3
[1639] Reduced Hospital Readmissions Through Personalized Care: Implementation of a Patient, Risk-Focused Hospital-Wide Discharge Care Center. — https://doi.org/10.1056/cat.24.0420
[1690] Evaluating the Performance and Safety of Large Language Models in Generating Type 2 Diabetes Mellitus Management Plans: A Comparative Study With Physicians Using Real Patient Records. — https://doi.org/10.7759/cureus.80737
[1697] Engaging hospitalized patients with personalized health information: a randomized trial of an inpatient portal. — https://doi.org/10.1093/jamia/ocy146
[1716] Preschool and Me: Educational-clinical linkage to improve health equity for children with developmental delays and disabilities from historically marginalized communities. — https://doi.org/10.1016/j.conctc.2024.101412
[1750] Cost-Benefit Analysis of Trans-Arterial Radio-Embolization with Y-90 Glass Microspheres Versus Drug-Eluting Bead Trans-Arterial Chemo-Embolization in Patients with Hepatocellular Carcinoma in Italy. — https://doi.org/10.1007/s00270-025-04214-4
[1752] Integrating breast cancer polygenic risk scores at scale in the WISDOM Study: a national randomized personalized screening trial. — https://doi.org/10.1186/s13073-025-01524-7
