# Bridging ancient wisdom and modern technology: an AI and multi-omics framework for three causes tailored treatment in personalized medicine

## TL;DR

This paper proposes combining AI and multi-omics data with traditional Chinese medicine principles to create a personalized, time-sensitive healthcare approach.

## Contribution

The novel contribution is integrating the TCTT principle with AI and multi-omics for a dynamic, personalized medicine framework.

## Key findings

- Multi-omics data can quantify individual, temporal, and environmental health dimensions.
- AI models can integrate complex interactions across omics, chronomics, and exposome data.
- The framework offers a translational roadmap for context-aware clinical decision support.

## Abstract

The ‘one-size-fits-all’ therapeutic model is inadequate to address individual patient variability, creating an urgent need for an integrative framework for precision medicine. The ‘Three Causes Tailored Treatment’ (TCTT) principle from traditional Chinese medicine offers a time-tested, holistic blueprint that simultaneously considers the individual, temporal, and environmental dimensions of health. Here, we argue that the synergy of artificial intelligence (AI) and multi-omics technologies is the key to transforming this ancient wisdom into a modern, quantitative clinical paradigm. We demonstrate how multi-omics data provides the foundational layers to quantify the TCTT principle—for instance, using integrated omics (e.g., genomics, proteomics, microbiome) to establish the individual’s molecular baseline (“Who”); chronomics to capture temporal fluxes (“When”); and the exposome to decipher the internalized environmental imprint (“Where”)—while AI-powered multimodal integration models their complex interactions. By synthesizing evidence across the disease continuum, this review provides a translational roadmap for building dynamic clinical decision-support systems, thereby charting a course toward truly personalized, time-sensitive, and context-aware healthcare.