Invasive mechanical ventilation (MV) is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, when MV alone is insufficient, extracorporeal lung support (ELS) is initiated. However, MV is recognised as potentially harmful, because inappropriate MV settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that MV is often not properly provided despite clear evidence and guidelines. Furthermore, treatment decisions by the healthcare providers, especially regarding MV and ELS, often remain incomprehensible to the patients and their relatives, since flow of information from caregivers to patients is challenged by a number of factors, including limited time and resources, communication problems as well as patients? capacity to comprehend and memorise information. The project proposed herein aims at clinically validating and extending our IntelliLung Artificial Intelligence Decision Support System (AI-DSS) designed to optimise MV and ELS settings to improve the care of ICU patients, alongside caregiver-patient communication. Thereby, best practice evidence-based MV and ELS within safer therapy corridors for longer periods, faster weaning from MV, and improved survival could be achieved - even in non-experienced hands. Additionally, this project will improve the information flow from caregivers to patients and relatives in the ICU setting. Therefore, we will develop a digital solution that allows automatic generation of an extensive plain-language information package for patients and their relatives, communicating highly individualised information on diseases and knowledge-based disease-management strategies, thus facilitating high-quality current and subsequent care through health literacy empowerment and patient-centredness. We will perform a retrospective and prospective multi-centre study to validate our IntelliLung AI-DSS and the patient information software.