Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.