Artificial Intelligence in Biomedical Engineering and Informatics

Artificial Intelligence (AI) is gradually changing the routine of medical practice, and the level of acceptance by the medical personnel is constantly increasing. Recent progress in digital medical data acquisition through advanced biosignal and medical imaging devices, machine learning and high-performance cloud computing infrastructures, push health-related AI applications into areas that were previously thought to be only the province of human experts. Such applications employ a variety of methodologies, including fuzzy logic, evolutionary computing, neural networks, or deep learning for producing AI-powered models that simulate human physiology; often called digital twins. In this context, digital twins can also be used t modeling an individual’s genomic makeup, physiological characteristics, and lifestyle to deliver personalized medicine. It is an ambitious paradigm looking at the human in an end-to-end approach, across all scales, unifying the virtual physiological human and the daily health behavior models and technologies. This approach is anticipated to disrupt clinical practice, since it offers a more individual focus than precision medicine, which typically involves specific groups within a greater population. Furthermore, twinning the human body can support medical diagnostics, follow-up monitoring, preventive medicine, therapy assessment, and many other domains.