Adding more information to an AI model naturally raises the question: as the system grows more complex, how can we trust its predictions? This invention addresses that challenge. It focuses on AI for infectious pulmonary diseases and proposes a framework for collecting and standardizing various patient data, including baseline info, lab results, images, interventions, and clinical outcomes. It also considers data access, anonymization, and encryption. Significantly, the framework tracks information across outpatient, emergency, and ICU settings, rather than just viewing the patient as a snapshot. It identifies trends in changing measurements, like heart rate and white blood cell counts, using techniques such as Adaptive Fourier Decomposition and an LSTM network to detect hidden patterns in time-series data. The invention can be seen as a health check for the AI itself, comparing predictions with actual outcomes and evaluating performance through measures like accuracy, precision, recall, and F1 score. Results are visualized for clarity, aiding researchers and clinicians in understanding the model’s performance. This approach provides a crucial counterbalance to the excitement about advanced medical AI, more data is only part of the story; clinicians also need ways to assess how well these algorithms perform. Along with the related multimodal model-building patent, this invention emphasizes an essential point: AI should not only assist doctors; it must also be open for evaluation.
Giving medical AI its own check-up
Patent number: CN 120221055 A
Inventor(s): Z. Han, X. Li, K. Cen, and Z. Liu
Citation: Z. Han, X. Li, K. Cen, and Z. Liu, “基于感染性肺部疾病诊断模型的临床性能验证系统” [Clinical performance validation system based on an infectious pulmonary disease diagnostic model], China Patent Application Publication CN 120221055 A, Jun. 27, 2025.
