Research on intellectual property rights and legal liability definition of artificial intelligence-assisted pediatric disease diagnosis system.

Background: Children's physiological development is immature, their symptoms are hidden and their diagnostic fault tolerance window is low, so they are the key protected population of public health. Although pediatric AI can alleviate the shortage of medical resources, the unclear definition of ownership and responsibility hinders its standardized development. Methods: This study adopts a mixed-method paradigm, separating exploratory analysis and hypothesis testing modules. Based on 89 hospital AI diagnosis documents, 17 medical dispute cases and relevant laws issued between 2021 and 2025, we adopted bibliometrics, case analysis, Delphi expert consultation, empirical regression and age-stratified subgroup analysis. Statistical conclusions only describe the correlation of variables, and do not deduce causality. The 0.85 interpretability threshold is only an exploratory single-center cutoff value and cannot serve as a universal mandatory industrial standard. Results: The data indicate that 73.0% of documents lack clear intellectual property ownership clauses, while 47.1% of algorithms score below 0.8 in interpretability. The average liability attribution cycle reaches 66.8 days, with 55.2% of cases exceeding the reasonable time limit. 64.7% of infants aged 0 ~ 3 years are involved in AI misdiagnosis disputes, and the medical adverse damage consequences with higher severity. Among the main causes of misdiagnosis, algorithm defects and hospital negligence accounted for 41.2% respectively, and the responsibility mismatch rate reached 64.7%. In terms of ownership, clinical data belongs to hospitals, and algorithms and software belong to enterprises. The calculation shows that if the interpretability of the algorithm rises above 0.85, the identification efficiency can be improved by 64.9% and the dispute correlation can be reduced by 30.6%. Conclusion: Clarify the rights and responsibilities of pediatric AI: the enterprise is responsible for the algorithm and the hospital is responsible for the operation; Give consideration to fairness and privacy. Construct algorithm description, IP evaluation and insurance mechanism to promote compliance application of 3A hospitals. This study is limited to single-center data, and its findings cannot be generalized without multi-center verification.