Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis. These works mainly focus on medical images as they use X-ray and ultrasound images as input for their models. In this study, we review the conducted works and compare the methods that use deep learning with the procedure that is commonly followed by a medical doctor for the RA diagnosis. The results show that 93% of the works use only image modalities as input for the models as distinct from the medical procedure where more patient medical data are taken into account. Moreover, only 15% of the works use direct explainability methods, meaning that the efforts for solving the trustworthiness issue of deep learning models were limited. In this context, this work reveals the gap between the deep learning approaches and the medical doctors’ practices traditionally applied and brings to light the weaknesses of the current deep learning technology to be integrated into a trustworthy context inside the existed medical infrastructures.