Beyond data and technology: the need for new thinking to enable the era of precision prevention - PubMed
3 days ago
- #AI in Health
- #Healthcare Innovation
- #Precision Prevention
- Global initiatives promote proactive health to reduce reactive healthcare burdens.
- Precision prevention targets causal pathways across disease continuum, surpassing conventional public health.
- Barriers to scaling precision prevention include misalignment with advances; need integrated frameworks.
- Core of precision prevention is individualised risk stratification using genomics, molecular markers, and exposomics.
- Machine learning/AI integrate heterogeneous data for personalised health trajectory predictions.
- Trustworthy AI requires transparency in model logic, assumptions, and performance.
- Discovery challenges: diagnostic classifications hide heterogeneity; precision phenotyping can reveal molecular insights.
- Evidence strategies for long-latency diseases include high-risk enrichment, surrogate endpoints, and adaptive monitoring.
- Stochastic variation and minimal exposures may encode individual-level signals.
- Shift to proactive healthcare needs coordinated stakeholder action to reform discovery, assessment, and implementation.