Artificial intelligence and machine learning are changing the landscape of healthcare and modern personalized precision medicine. The increasing availability of health data, including patient medical records also obtained by wearable sensors, medical imaging, health insurance claims, surveillance, together with the rapid progress of machine learning algorithms and analysis techniques, are gradually enabling doctors for better diagnosis, improve disease surveillance, facilitating early disease detection, uncovering novel treatments and drug-interaction, detect false alarms and over-diagnosis, and creating an era of truly personalized medicine.
A great challenge is build better modeling tools for integrating human expertise and artificial intelligence techniques to exploit big data in healthcare, and formulate hypothesis about how the human organisms act in health and illness.
The main areas of machine learning and AI applications in healthcare are: personalized precision medicine, analysis and interpretation of radiology images, automated diagnosis, prescription preparation, clinical workflow monitoring, patient monitoring and care, discovery of new drugs, predicting the impact of gene edits, treatment protocol development, early diagnoses of diseases. In this context, AI techniques can play a crucial role to deal with such amount of heterogeneous, multi-scale and multi-modal data. Some examples of techniques that are gaining attention in this domain include deep learning, domain adaptation, semi-supervised approach, time series analysis and active learning. Even though the use of AI and the development of ad-hoc techniques are gaining increasing popularity in the health domain, we can witness that a significant lack of interaction between domain experts and AI researchers still exists. The workshop provides a venue for the community to promote collaborations and present and exchanges ideas, practices and advances specific to AI use in the particularly challenging area of health, precision medicine and wellbeing. The goal is to bring people in the field cross-cutting information management and medical informatics to discuss innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare, public health, and everyday wellness, with clinical, physiological, imaging, behavioral, environmental, and omic data, and data from social media and the Web.