Xiaochen Qin | Searcharxiv

arXiv2025

Design and Evaluation of an AI-DrivenPersonalized Mobile App to Provide MultifacetedHealth Support for Type 2 Diabetes Patients inChina

Type 2 diabetes patients in China face many significant challenges in patient-provider communication and self management In light of this, this work designed,implemented,and evaluated an AI-driven, personalized, multi-functional mobile app system named T2MD Health. The appintegrates real-time patient- provider conversation transcription,medical terminology interpretation, daily health tracking, and adata-driven feedback loop. We conducted qualitative interviewswith 40 participants to study key user needs before systemdevelopment and a mixed- method controlled experiment with 60participants after to evaluate the effectiveness and usability ofthe app. Evaluation results showed that the app was effective inimproving patient-provider communication efficiency, patientunderstanding and knowledge retention,and patient selfmanagement, Patient feedback also revealed that the app has thepotential to address the urban-rural gap in the access to medica!consultation services to some extent, Findings ofthis study couldinform future studies that seek to utilize mobile apps andartificial intelligence to support patients with chronic diseases.

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arXiv2023

MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems

Label-free cell classification is advantageous for supplying pristine cells for further use or examination, yet existing techniques frequently fall short in terms of specificity and speed. In this study, we address these limitations through the development of a novel machine learning framework, Multiplex Image Machine Learning (MIML). This architecture uniquely combines label-free cell images with biomechanical property data, harnessing the vast, often underutilized morphological information intrinsic to each cell. By integrating both types of data, our model offers a more holistic understanding of the cellular properties, utilizing morphological information typically discarded in traditional machine learning models. This approach has led to a remarkable 98.3\% accuracy in cell classification, a substantial improvement over models that only consider a single data type. MIML has been proven effective in classifying white blood cells and tumor cells, with potential for broader application due to its inherent flexibility and transfer learning capability. It's particularly effective for cells with similar morphology but distinct biomechanical properties. This innovative approach has significant implications across various fields, from advancing disease diagnostics to understanding cellular behavior.

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