The rise of health care AI raises concerns over whether patent disclosure supports reproducibility and legal validity. This study analyzes 865 granted medical AI patents (2015-2025) from the US, China, and the EU using a five-dimensional framework (algorithm transparency, training data accessibility, model reproducibility, result verifiability, and mathematical support) implemented through NLP-assisted expert scoring. Results suggest limited technical transparency; approximately 40% of patents score zero in at least two dimensions. Performance varies significantly: algorithm transparency is relatively strong (>60% score 2), while training data accessibility is less prevalent (4.6% score 2) and mathematical support is frequently omitted (39.4% score 0). Statistical testing indicates US patents significantly outperform Chinese patents (p < 0.001), while EU results remain exploratory (N = 31, mean 6.2). These patterns appear associated with institutional factors, strategic applicant behaviour, and technical complexity. Such limitations may pose risks to enforceability and market development, highlighting the need for targeted disclosure improvements. This study contributes a replicable framework for translating legal standards into measurable indicators, providing cross-jurisdictional evidence to guide examination, litigation, and policy refinement in medical AI governance.
Xiao et al. (Mon,) studied this question.