Human-Centric Clinical AI: A Structural Data Validation Approach for Human-Reliable Medical Inference

Researchers increasingly rely on Clinical Artificial Intelligence (CAI) to derive predictive insights from massive observational datasets. However, the paradigm of Big Medical Inference often conflates statistical volume with clinical representativeness. This study argues that when methodological rigor is sacrificed for data scale, AI models risk institutionalizing historical biases, thereby compromising patient safety and public health policy integrity. We propose a precautionary, multi-level auditing framework designed to assess the structural architectural integrity of large-scale clinical datasets prior to computational deployment. Rather than contesting specific clinical outcomes, our approach establishes a quantitative prerequisite for CAI: the validation of demographic representativeness and baseline case distribution against official national benchmarks. To demonstrate the validity of our approach, we applied our framework to a prominent, high-profile case study. Our check revealed a tripartite structural divergence: a 32.5% demographic deficit in high-risk elderly (aged ≥ 65 years) strata, an aggregate of approximately 26% cancer incidence suppression, and a 45.1% deflation of expected cases in non-exposed groups. These discrepancies demonstrate that even massive datasets can be fundamentally misaligned with clinical reality. We conclude that structural validation is not an elective procedure but an ethical imperative for Human-Centric Clinical AI. By re-establishing this hierarchy of validation, we ensure that the intelligence of automated AI systems remains subordinate to the structural truth of the data, thereby transitioning from a reliance on Big Data alone toward a more robust, ethically sound, and authentic practice of clinical knowledge.