The introduction of Artificial Intelligence (AI) Systems in the design of new aeronautical products impacts not only the project architecture regarding the hardware and software but also inserts new layers of complexity into Systems Engineering methods to evaluate the entire product life cycle. More specifically related to the end users within the scope of the study, the pilots, the inclusion of AI Systems also impacts the human factors aspects addressed in Systems Engineering methods, as well as the human-machine interaction. Issues such as information ambiguities, lack of pilot situational awareness and system understanding can be presented in various flight events and are concerns of the aeronautical industry and certification authorities. Human-AI Teaming (HAT) concepts are the basis of understanding how the flight crew can use AI systems in aviation, enabling them to achieve their full potential. As such, this paper reviews the main AI Systems research in aviation and their relationship with human factors and systems engineering. It discusses the main outcomes, challenges, and recommendations for human-AI systems integration. The research shows that there has been a considerable increase in the study in the last decade, most focused on machine learning applications that could be used not only in the flight deck assisting the pilot but also as a ground or real-time analysis of the workload and situational awareness. It is discussed how these AI Systems could be even more implemented in aviation in a specific context of flight operations, utilizing current patterns, for example, from the flight manuals, that could feed data regarding AI Systems, and therefore being part of the assistance of the flight crew.