A novel statistical framework combining artificial intelligence with traditional analysis methods identified previously undetected Alzheimer's disease pathways in single-cell RNA sequencing data from the ROSMAP cohort. The hybrid approach uses ChatGPT-4o to assess gene-disease evidence strength, mapping responses to informative priors while treating secondary parameters as frequentist variables. Applied to genome-wide data, the method discovered biologically coherent pathways including gamma-secretase pathways that conventional analysis missed. This represents a significant methodological advance in genomics research, where Bayesian inference has been underutilized despite its advantages in small-sample settings due to the computational burden of establishing reliable priors across thousands of parameters. The framework's ability to control Type I error rates while maintaining high statistical power could accelerate discovery in Alzheimer's genomics and other complex diseases. However, the approach relies on AI interpretation of biological evidence, introducing potential biases from training data. The method requires validation across diverse datasets and diseases before widespread adoption. As this is a preprint awaiting peer review, the statistical properties and biological interpretations may undergo revision. The work appears paradigm-shifting for computational genomics methodology.