Dear Colleagues,

There has been a recent revolution in the application of machine learning and deep learning within healthcare, with interest in this area increasing exponentially at both medical society meetings and computer science conferences. Unlike prior attempts at medical AI and computer-aided diagnosis, these algorithms do not rely on predetermined features and can discern patterns in the data that would be impossible for an individual to detect.

The healthcare domain provides rich data that these algorithms can draw upon, including clinical notes, vital signs, laboratory values, genomic data, pathology, radiological images, and medical sensors, just to name a few. In addition, multi-modal and omics data may be applied to solve clinical problems. These data can be used to achieve multiple goals, including diagnosing diseases, prognosticating clinical outcomes, determining responses to therapy, patient monitoring, and drug as well as device development. In addition, these technologies provide researchers with the opportunity to enhance their understanding of disease pathogenesis, leveraging both large volumes of data and advanced machine learning techniques.

These developments allow for new frontiers in medicine. These include learning healthcare systems that improve with time as they incorporate increasing volumes of multimodal data from diverse patient populations. They also enable personalized medicine, the tailoring of healthcare to individual patients. Meanwhile, it is crucial that these algorithms remain robust to perturbations in the input data while remaining trustworthy, ethical, and free of bias. These techniques need to generalize well to heterogeneous patient populations, while maintaining and ultimately improving their performance in the populations in which they were developed. This Special Issue welcomes both original research articles and review articles that investigate the state of the art in machine learning and deep learning applied to healthcare. 

Dr. Hersh Sagreiya Sagreiya
Guest Editor

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• machine learning
• deep learning
• medicine
• health
• disease diagnosis
• disease prognostication
• treatment effectiveness
• electronic medical records
• medical informatics