Diagnostic medical imaging and the interpretation of the imaging results pose a great challenge for the medical profession as the final conclusions are highly susceptible to human error and subjectivity. The necessity for standardization of interpretation of medical images is very necessary to bypass these problems. The only way of achieving this is using a methodology which excludes the human eye and employs artificial intelligence. However, another challenge is selecting the most suitable AI algorithm fit for the challenging task of imaging results interpretation. This study was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines published in 2020. Research was done using PubMed, ScienceDirect and Google Scholar databases where the key inclusion criteria were language, journal credibility, open access to full-text publications and the most recent papers. In order to focus on only the most recent research, only the papers published in the last 5 years were evaluated. The search through PubMed, ScienceDirect and Google Scholar has yielded 81, 205, and 520 papers respectively. Out of this number of papers, 26 of them have met all of the inclusion criteria and were included in the research. The observed accuracies of the models and the overall rising interest in the topic denote that this field is rapidly growing and has a great potential to be applied in daily medical practice in the future.