AI and Personalized Medicine: Stratified and Individualized Treatment in Clinical Practice
Key Finding
Contemporary reviews and early clinical implementations show that AI models integrating genomic, imaging, and clinical data can stratify patients into treatment-response subgroups and support individualized therapy selection, with promising results in oncology and chronic disease management. However, large-scale prospective trials demonstrating consistent improvements in survival or quality of life across broad populations are still limited.
Executive Summary
Personalized medicine seeks to tailor treatment based on individual biological and clinical characteristics. Reviews in 2025 describe how AI integrates multi-omics, imaging, laboratory, and real-world data to generate patient-specific risk predictions and treatment-response profiles, enabling more precise selection and sequencing of therapies. AI-driven "digital twins" and multiscale models can simulate disease trajectories and responses to different treatment options, potentially allowing dynamic treatment adjustment as new data accrue.
In oncology and autoimmune disease, machine-learning models have been used to predict treatment response, toxicity, and prognosis, sometimes outperforming traditional risk scores and informing clinical trial design. Nonetheless, the translation of these models into routine practice faces challenges related to data quality, model transparency, integration with clinician workflows, and the need for prospective validation in diverse populations.
Detailed Research
Methodology
Evidence comes from narrative and systematic reviews of AI in personalized medicine, focusing on oncology, rheumatology, and chronic disease management, as well as early-phase clinical trials and pilot implementations. These reviews synthesize algorithm-development studies, small clinical trials, and proof-of-concept implementations.
AI techniques include supervised learning, unsupervised clustering, deep learning on imaging and genomic data, and digital-twin modeling that combines mechanistic and data-driven approaches.
Key Studies
Revolutionizing Personalized Medicine Using AI (2025)
- Design: Narrative review
- Sample: AI applications across oncology and chronic disease
- Findings: AI constructs patient-specific digital twins integrating genomics, imaging, lab values, and lifestyle data to support early detection, prognostication, and individualized treatment planning.
- Clinical Relevance: Enables refined therapy selection and monitoring
Machine Learning Techniques for Personalized Medicine in Autoimmune Disease (2021)
- Design: Systematic review
- Sample: Autoimmune rheumatic diseases
- Findings: ML applications predict disease progression and treatment response, improving stratification beyond traditional clinical indices.
- Clinical Relevance: Potential for more precise therapy selection
AI and Innovation in Clinical Trials (2025)
- Design: Perspective article
- Sample: Clinical trial design
- Findings: AI can optimize eligibility criteria, stratify patients, and guide adaptive randomization, enhancing personalization of trial interventions and informing real-world care.
- Clinical Relevance: Bridges trial insights to clinical practice
Clinical Implications
For osteopathic physicians, AI-enabled personalized medicine can support more nuanced risk stratification and therapy selection for patients with complex, multi-system conditions, complementing osteopathic assessment of structure–function relationships.
In primary care and specialty settings, DOs can use AI-generated risk profiles and response predictions as one input to shared decision-making, integrating them with hands-on examination, patient values, and non-pharmacologic options such as OMT.
Limitations & Research Gaps
Most evidence is preclinical or from early-phase trials; large-scale, real-world studies demonstrating robust outcome improvements and cost-effectiveness are still needed. Data sparsity, bias, and interoperability challenges can limit generalizability.
There is no osteopathy-specific research on integrating structural exam findings into AI-driven personalized medicine models.
Osteopathic Perspective
Osteopathic medicine emphasizes individualized, whole-person care; AI-based personalization aligns conceptually but must be grounded in robust evidence and used to augment—not replace—clinical judgment.
DOs are well positioned to contextualize AI-generated risk and treatment recommendations within a holistic framework that includes body–mind–spirit unity, structural assessment, and the body's self-regulatory capacity.
References (2)
- Khan M, Alzubaidi H, et al. “Revolutionizing Personalized Medicine Using Artificial Intelligence.” Clinica Chimica Acta, 2025;562:119-130. DOI: 10.1016/j.cca.2025.06.012
- Koczkodaj WW, et al. “Machine Learning Techniques for Personalized Medicine in Autoimmune Rheumatic Diseases.” Frontiers in Pharmacology, 2021;12:720694. DOI: 10.3389/fphar.2021.720694