到 2026 年，精准医疗预测旨在在症状出现之前，就预测个体在三大主要年龄相关疾病群——癌症、心血管、与神经退行性疾病——上的风险。由于这些疾病常有 20 年以上的潜伏期，模型可以锁定早期的生物趋势，例如免疫衰老（immunosenescence）与慢性发炎（「inflammaging」）。

此方法把老化「时钟」（全身与器官特异）与蛋白质生物标记结合，再加入 AI 从视网膜扫描等影像中读出的讯号。这些资料层再与电子病历（结构化病历与非结构化笔记、检验、扫描）、基因、穿戴式感测器、与环境暴露融合，以估计不只是风险，还有病程时间轴——疾病的「何时」——超越现今的多基因风险分数。

可行性取决于前瞻性试验能否证明介入措施会让同一套老化指标移动，并降低风险。生活型态杠杆——抗发炎饮食、规律运动、与高品质睡眠——能降低风险，尤其在个人看见量化脆弱性时；而可调节免疫与发炎的药物（包含 GLP‑1 疗法）也正在出现。对阿兹海默症风险而言，像 p‑tau217 这类血液生物标记可与脑部与全身时钟搭配，用来验证改善。

By 2026, precision medical forecasting aims to predict individual risk for the three dominant age-related disease groups—cancer, cardiovascular, and neurodegenerative—well before symptoms. Because these conditions often incubate for 20+ years, models can target early biologic trends such as immunosenescence and chronic inflammation (“inflammaging”).

The approach combines aging “clocks” (whole-body and organ-specific) with protein biomarkers and AI-read signals from images such as retinal scans. These layers are fused with electronic records (structured notes, labs, scans), genetics, wearables, and environmental exposures to estimate not only risk but the time course—the “when” of disease—beyond today’s polygenic risk scores.

Actionability hinges on prospective trials showing that interventions shift the same aging metrics and lower risk. Lifestyle levers—anti-inflammatory diet, frequent exercise, and high-quality sleep—can reduce risk, especially when people see quantified vulnerability; drugs that modulate immunity and inflammation, including GLP‑1 therapies, are emerging. For Alzheimer’s risk, blood biomarkers such as p‑tau217 could be paired with brain and body clocks to verify improvement.