“…Recent developments in NLP techniques have helped the automation of this process through machine learning and, in particular, deep learning algorithms [ 82 , 83 ]. Symptoms, patient demographics, clinical data, algorithms, performance, and limitations are identifiable in the texts by properly trained models, which can obtain comparable accuracy to humans at a much faster rate, making it finally possible to monitor the enormous volume of the literature produced [ 84 ]. The resulting structured data can be exploited to enrich knowledge graphs (KGs) [ 85 - 87 ], which provide a means to represent and formalize information [ 85 , 88 ], analytical, relational, and inferential investigations and fill the knowledge gaps in the community.…”