Abstract
Nowadays, patients are often in a hurry to know their analysis and concise explanations their medical images pending the doctor's decision. Most of the time, doctors can make mistakes leading to unpredictable damage in patients. In order to minimize medical errors by fostering collaboration between physicians and/or patients, we propose in this paper, as a first contribution, a medical social network destined to gather patients' medical images and physicians' annotations expressing their medical reviews and advice. As the volume of comments is very important, analysis of opinions becomes an impossible task and requires automatic processing to extract relevant information collected from the comments of specialists. For this purpose, we propose a second contribution of producing summaries of comments containing most current conditions and relevant words prescribed by doctors. Furthermore, this extracted information will present a new and robust input for image indexation enhanced methods. In fact, significant extracted terms will be used later to index images in order to facilitate their search through the underlying social network. To overcome the above challenges, we propose an approach which focuses on algorithms mainly based on statistical methods and external semantic resources destined to filter selected extracts information.