The rarity is the most influential obstacle in recognizing, diagnosing and treating rare diseases. Among thousands of rare diseases, the majority affect fewer than 1 patient in a 1 million which is difficult to trace because even experienced doctors with a lot of patient contact never see a single patient as such in their lifetime. Indeed, reaching such a point is logistically more complicated, if not sometimes impossible in such a wide and widely dispersed population. The studies to test such individuals with rare diseases becomes difficult to study mainly because of the lack of statistical power. Here Machine Learning can become a great and effective tool in improving treatment of rare diseases and in future studies for example machine learning can accelerate drug development. Machine learning in medicine is used in the areas of rare diseases and points to scientific holes or potential fields of study.

According to a study published in Orphanet Journal of Rare Diseases future technology especially Machine Learning and Artificial Intelligence can improve timely diagnosis, treatment and research of rare diseases. There were total 211 studies included in the final analysis among which 74 rare diseases were investigated. Machine learning was most commonly used in diagnostic (40.8 percent) or prognostic research (38.4 percent). The authors noted that because classification and prediction are traditional machine learning applications, it made sense for the most common uses to be diagnosis and prognosis.

Despite its potential to improve the quality of patient care, there has been no comprehensive review of the use of machine learning in the field of rare diseases. For example, it is unclear where machine learning is used for rare diseases, which algorithms are typically used, which medical applications are studied (e.g. diagnosis , prognosis or treatment) and what type of input data is used. By mapping the current use of machine learning in medicine and current research activity, it can guide future work and help facilitate successful application of machine learning in rare diseases.