Artificial Intelligence (AI) is transforming humans’ activities through continuous collaboration in domains where AI-supported decisions affect humans and societies. In this context, the focus should shift toward the development of Symbiotic AI (SAI) systems that support humans rather than replace them. This contribution presents a set of Success Criteria (SC) that can be used to evaluate such SAI systems by determining whether the requirements of symbiosis are met. The paper also includes a preliminary validation of the criteria through a pilot user study.

The rapid spread of Large Language Models (LLMs) is influencing many contexts, shaping the way in which humans conduct activities. LLMs can represent a promising instrument in the medical field to support physicians in creating therapy. This contribution aims at presenting the employment of LLMs for creating exercises addressed to speech therapies investigating whether these can be an effective tool to support physician in this activity. This contribution presents the experimental setup of tests conducted with LLMs along with an overview of the obtained results showing the potential and limitations of this technology.

Eating disorders are disturbances in people’s eating behaviours that can influence their social and private lives, with consequences related to mental and physical health. Therefore, they must be timely and appropriately treated by professionals and, in this context, the employment of technology can improve the efficiency of treatments allowing remote assistance, continuous monitor, and less stress than the traditional medical approach.

Nowadays, the rapid spread of Artificial Intelligence (AI) in the daily lives of individuals is raising multiple challenges and issues. The latter must be timely and effectively addressed since they have a substantial impact on numerous domains with implications concerning the technicalities, typically faced by computer science, and also to multiple areas of society, such as law, ethics, psychology, etc. The PhD project presented in this manuscript aims at finding new human-centered methodologies and techniques, belonging to the Human-Computer Interaction (HCI) discipline, that can be applied to the design and development of AI to foster the symbiotic relationship between machines and humans. The project deals with Symbiotic Artificial Intelligence (SAI), which has the goal of supporting humans in executing their tasks without replacing them. The research aims at finding the intersection between Software Engineering (SE), HCI, and AI to create a framework supporting and providing guidance to all the parties involved in the process of creating effective SAI systems.

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.