Machine Learning in Disease Diagnosis

Dear Colleagues,

Machine learning (ML) is transforming disease diagnosis by extracting clinically actionable patterns from heterogeneous healthcare data, enabling earlier detection, improved phenotyping, and more reliable decision support. This Special Issue, "Machine Learning in Disease Diagnosis," aims to collect state-of-the-art methods and high-impact applications that bridge methodological innovation and clinical utility across diverse diseases and diagnostic settings. We welcome contributions on ML/DL for screening and diagnosis; multimodal learning that fuses medical imaging, laboratory tests, and electronic health records (EHR); and models leveraging longitudinal or time-series data for early warning, progression modeling, and differential diagnosis. We also encourage approaches that address limited or noisy labels (e.g., self-supervised or transfer learning) and that provide explainable AI (XAI) with transparent, clinically actionable interpretations. Studies strengthening trustworthiness through robustness analysis, uncertainty quantification, calibration, and rigorous internal/external validation are especially encouraged, as are comparative benchmarking and reproducible evaluation protocols, shared openly when feasible. Submissions addressing responsible deployment—bias and fairness assessment, privacy-aware learning, and regulatory/ethical considerations—are also within scope.

Prof. Dr. Chi-Jie Lu
Dr. Yi-Wei Kao
Guest Editors

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