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Depression remains a prevalent condition in primary care due to its heterogeneous symptoms. To support early screening, this study presents DepreScan, an interactive web-based clinical decision support system powered by machine learning and explainable artificial intelligence. Trained on a representative health survey dataset, DepreScan uses interpretable models and multiple explanation techniques—including SHAP plots and simplified decision trees—to assist healthcare practitioners in screening depression risk. A mixed-methods user study with 16 clinicians assessed the system’s trustworthiness, usability, and perceived clinical utility. Results indicate moderate to high acceptance and trust, particularly for SHAP-based global feature explanations, with differences in measured trust between two related questionnaires. The study highlights the importance of aligning ML explainability with healthcare professionals’ mental models and the findings inform the design of user-centered XAI tools for mental health decision support in primary care.

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Trustworthiness and Medical Usefulness of Explainability Techniques in ML-Supported Depression Screening Within Primary Care

  • 会议论文

  • 发表日期：

    2026年

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  • Guilherme Gryschek,
  • Luis Quintero,
  • Alejandro Kuratomi

摘要

Depression remains a prevalent condition in primary care due to its heterogeneous symptoms. To support early screening, this study presents DepreScan, an interactive web-based clinical decision support system powered by machine learning and explainable artificial intelligence. Trained on a representative health survey dataset, DepreScan uses interpretable models and multiple explanation techniques—including SHAP plots and simplified decision trees—to assist healthcare practitioners in screening depression risk. A mixed-methods user study with 16 clinicians assessed the system’s trustworthiness, usability, and perceived clinical utility. Results indicate moderate to high acceptance and trust, particularly for SHAP-based global feature explanations, with differences in measured trust between two related questionnaires. The study highlights the importance of aligning ML explainability with healthcare professionals’ mental models and the findings inform the design of user-centered XAI tools for mental health decision support in primary care.