As new imaging technologies, such as Digital Radiograph (DR), advance, radiologists nowadays are able to detect smaller nodules than before. However, inter-observer variations exhibited in diagnosis still remain as critical challenges that need to be studied and addressed. In this research, inter-observer variation of pulmonary nodule marking and characterizing on DR images was studied in two phases, with the first phase focused on the analysis of inter-observer variations, and the second phase focused on the reduction of variations by using a computer system (IQQA(R)-Chest) that provides intelligent qualitative and quantitative analysis to help radiologists in the softcopy reading of DR chest images. Large inter-observer variations in pulmonary nodule identification and characterization on DR chest images were observed, even between expert radiologists. Experimental results also showed that less experienced radiologists could greatly benefit from the computer assistance, including substantial decrease of inter-observer variation and improvement of nodule detection rates. Moreover, radiologists with different levels of skillfulness may achieve similar high level performance after using the computer system. The computer system showed a high potential for providing a valuable assistance to the examination of DR chest images, especially as DR is adopted to screen large populations for lung cancer.