At the 2015 Davos forum, among the most impressive achievements predicted for 2025 are regular internet access for 90 However, it is clearly that the role of AI is becoming crucial for many fields of activity, including creative ones. One topic of particular importance is the use of AI in healthcare, and the COVID 19 pandemic has confirmed this. According to officials at Moderna, the rapid development of the vaccine was largely made possible by the application of artificial intelligence. More and more widespread are AI-based approaches in medical image processing (in particular - in cancer detection), remote consultations, diagnosis. Such approaches have also been applied by researchers at the V.Andrunachievici Institute of Mathematics and Computer Science in the development of intelligent information systems for medicine. In what follows, we will briefly review them. Solutions in the SonaRes system were developed to provide the possibility to use the experience of experts gathered in the knowledge base of the system, to consult the annotated images, similar to the ones examined and guide the examination process, adapting to the different level of experience of the doctor. An essential feature is assistance in the preparation of the report ensuring compliance with a single standard. It is also important to prevent possible errors in the examination process (such as omitting some important aspects or characteristics in the examination, admitting inaccuracy in the formulation of the conclusion, etc.) SonaRes provides annotated images, help, illustrations and explanations in difficult cases especially important for inexperienced young people. offers the possibility of being used in training. Information System Support for Cerebrovascular Accident prophylaxis. The project within the State Program Systemogenesis of risk factors, optimization of the health service, sustainable evaluation and mathematical modeling of stroke was focused on the development of personalized mathematical prediction models (including for small volume passive samples). The computer system STROKE.MD was developed to support the collection and processing of data and the development of personalized prediction models. Machine learning based methods were used to develop the predictive models, especially those incorporated in the WEKA system. AI Based Multilayered Approach forManagement ofMass Casualty Situations. The project jointly developed with researchers from Germany, Romania, Croatia and the USA aimed to develop software for mobile devices with a simple user interface (via voice recording) to collect and organize primary medical data of victims and create DSS systems for the efficient placement and transport of victims, providing guidance for rapid transport based on innovative AI inference frameworks and transport systems but also for the medical evaluation of stabilized victims (based on ultrasound characteristics) during transport or in clinical conditions.