AI-Based Automated Recognition and Detection in Healthcare

Dear Colleagues,

AI-based computer-aided diagnosis relies on the recognition and detection of disease symptoms from medical data. Evaluating AI models is crucial for driving progress and fostering competition. While traditional evaluation metrics like accuracy, sensitivity, and specificity are widely understood, they often overlook biases and noise present in the training data. This lack of consideration leads to ambiguity, making it challenging to compare and compete with AI-based solutions. In this Special Issue, we seek papers that surpass standard performance reporting. This can be achieved through preprocessing methods that identify biases and noise in the training data or through post-processing techniques that enhance performance measures with explainability. For this SI, we specify medical data as coming from sensors and taking the form of images and physiological signals.

Dr. Ningrong Lei
Dr. Oliver Faust
Prof. Dr. U Rajendra Acharya
Guest Editors

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