This review maps AI's role in personalized medicine, from biomarker discovery to clinical decision support, while noting unresolved ethical, legal, and translation barriers

The review defines the goal of personalized medicine as basing prevention, diagnosis, prognosis, and treatment on the biological, clinical, environmental, and behavioral specificities of the patient in front of the clinician, rather than the average patient of a trial population. Relative to strategies built on trial-population averages, this framing places individual specificity at the center and provides a shared conceptual starting point for discussing which data and technologies can support personalization. This is a review-level conceptual statement drawn from the article's description of personalized medicine; it is a framing argument rather than empirical data.

The review states that AI is one of the key technologies driving personalized medicine because it can manage data that is often too big, too complex, or too multidimensional to be comfortably handled by traditional statistical methods. This positions AI not as a replacement for the doctor but as a means of handling high-dimensional, complex data, providing a basis for understanding its value in tasks such as biomarker discovery, disease subtyping, and risk prediction. This is a review-level statement about technology positioning; the text provides no specific dataset sizes or performance figures.

The review covers applications including biomarker discovery, disease subtyping, risk prediction, early diagnosis, treatment-response modelling, pharmacogenomics, digital pathology, longitudinal monitoring, and clinical decision support. By placing these dispersed application areas within a single framework, it helps readers see the breadth of AI in personalized medicine rather than a single step in the pipeline. This is a review-level list of application domains, a scope description without per-item research evidence or outcome data.

The review emphasizes that AI has not fully matured and is still experiencing its adolescence in personalized medicine, and notes that ethical and legal issues remain unresolved and that obstacles stand between research potential and clinical practice. By stating maturity and translation gaps alongside application prospects, it prevents readers from equating research-stage potential with clinical readiness. This is a review-level judgment about the state of the field; the text provides no quantitative assessment or systematic evidence grading.

This review is aimed at readers who want an overall orientation to AI in personalized medicine, and it applies to settings where one is building a conceptual framework, identifying application areas, and learning about open questions. It sets the goal of personalized medicine as based on individual biological, clinical, environmental, and behavioral specificities rather than trial-population averages, and positions AI as an aid that helps doctors access more information than they could retain within their minds all at once, rather than replacing physician decision-making. For researchers needing specific methodological details, dataset characteristics, or clinical validation results, this article offers a domain map rather than an operational guide.

Because the loaded text is an incomplete review description lacking the main body, figures, and references, it is not possible to judge the specific evidence strength of each application area, the designs and sample characteristics of the cited studies, or the details of the ethical and legal discussion and the obstacles mentioned. Readers interested in the actual performance of a specific application, such as pharmacogenomics or digital pathology, would still need to consult the original article. In addition, the maturity judgment that the field is still in its adolescence is the review authors' overall assessment, and its basis is not elaborated in the loaded text.