  Artificial intelligence (AI) has acquired notorious relevance in modern
computing as it effectively solves complex tasks traditionally done by humans.
AI provides methods to represent and infer knowledge, efficiently manipulate
texts and learn from vast amount of data. These characteristics are applicable
in many activities that human find laborious or repetitive, as is the case of
the analysis of scientific literature. Manually preparing and writing a
systematic literature review (SLR) takes considerable time and effort, since it
requires planning a strategy, conducting the literature search and analysis,
and reporting the findings. Depending on the area under study, the number of
papers retrieved can be of hundreds or thousands, meaning that filtering those
relevant ones and extracting the key information becomes a costly and
error-prone process. However, some of the involved tasks are repetitive and,
therefore, subject to automation by means of AI. In this paper, we present a
survey of AI techniques proposed in the last 15 years to help researchers
conduct systematic analyses of scientific literature. We describe the tasks
currently supported, the types of algorithms applied, and available tools
proposed in 34 primary studies. This survey also provides a historical
perspective of the evolution of the field and the role that humans can play in
an increasingly automated SLR process.
