An N-of-1 Artificial Intelligence Ecosystem for Precision Medicine

Abstract

Artificial intelligence in medicine is built to serve the average patient. By minimizing error across large datasets, most systems deliver strong aggregate accuracy yet falter at the margins: patients with rare variants, multimorbidity, or underrepresented demographics. This average patient fallacy erodes both equity and trust. We propose a different design: a multi-agent ecosystem for N-of-1 decision support. In this environment, agents clustered by organ systems, patient populations, and analytic modalities draw on a shared library of models and evidence synthesis tools. Their results converge in a coordination layer that weighs reliability, uncertainty, and data density before presenting the clinician with a decision-support packet: risk estimates bounded by confidence ranges, outlier flags, and linked evidence. Validation shifts from population averages to individual reliability, measured by error in low-density regions, calibration in the small, and risk-coverage trade-offs. Anticipated challenges include computational demands, automation bias, and regulatory fit, addressed through caching strategies, consensus checks, and adaptive trial frameworks. By moving from monolithic models to orchestrated intelligence, this approach seeks to align medical AI with the first principle of medicine: care that is transparent, equitable, and centered on the individual.

Figure 1:

Summary panel: error vs. density (tail shaded), risk–coverage (solid = monolith; dashed = multi-agent), tail calibration (ECE), $x_3$ diagnostic for cluster D, ROC for cluster D (ΔAUC = 0.406; p ≈ 3.9 × 10⁻⁸), and population vs. tail bars (overall ΔAUC = 0.013; tail ΔAUC = 0.035).