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This article presents a methodical approach for improving quadratic time-frequency distribution (QTFD) methods by designing adapted time-frequency (T-F) kernels for diagnosis applications with illustrations on three selected medical applications using the electroencephalogram (EEG), heart rate variability (HRV), and pathological speech signals. Manual and visual inspection of such nonstationary multicomponent signals is laborious especially for long recordings, requiring skilled interpreters with possible subjective judgments and errors. Automated assessment is therefore preferred for objective diagnosis by using T-F distributions (TFDs) to extract more information. This requires designing advanced high-resolution TFDs for automating classification and interpretation. As QTFD methods are general and their coverage is very broad, this article concentrates on methodologies using only a few selected medical problems studied by the authors.