A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

BACKGROUND: Machine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust. OBJECTIVE: This study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, designed to empower health care professionals with a clear understanding of AI-generated predictions and recommendations for diabetes care. XAI4Diabetes not only delivers diabetes risk predictions but also furnishes easily interpretable explanations for complex machine learning models and their outcomes. METHODS: XAI4Diabetes features a versatile multimodule explanation framework that leverages machine learning, knowledge graphs, and ontologies. The platform comprises the following four essential modules: (1) knowledge base, (2) knowledge matching, (3) prediction, and (4) interpretation. By harnessing AI techniques, XAI4Diabetes forecasts diabetes risk and provides valuable insights into the prediction process and outcomes. A structured, survey-based user study assessed the app’s usability and influence on participants’ comprehension of machine learning predictions in real-world patient scenarios. RESULTS: A prototype mobile app was meticulously developed and subjected to thorough usability studies and satisfaction surveys. The evaluation study findings underscore the substantial improvement in medical professionals’ comprehension of key aspects, including the (1) diabetes prediction process, (2) data sets used for model training, (3) data features used, and (4) relative significance of different features in prediction outcomes. Most participants reported heightened understanding of and trust in AI predictions following their use of XAI4Diabetes. The satisfaction survey results further revealed a high level of overall user satisfaction with the tool. CONCLUSIONS: This study introduces XAI4Diabetes, a versatile multi-model explainable prediction platform tailored to diabetes care. By enabling transparent diabetes risk predictions and delivering interpretable insights, XAI4Diabetes empowers health care professionals to comprehend the AI-driven decision-making process, thereby fostering transparency and trust. These advancements hold the potential to mitigate biases and facilitate the broader integration of AI in diabetes care.