Material andMethods Atlas Based Segmentation (ABS) was utilized for automatic volume delineation.Volume metrics and DICE coefficient scores were compared between multiple manual delineation, ABS and ABS with post processing.Automatic planning was achieved by python code in the Raystation treatment planning environment.Initial optimization objectives were determined by a mindifference optimization of database entries from previous clinical plans.Plan evaluation checks against both standard guidelines and previous plan quality scores are produced through python code and inbuilt look up tables.Non-linear scoring systems are incorporated for total plan scores that provide score weighting to crucial structures.Adaptive planning and dose tracking is achieved in a Varian-Mosaiq-Raystation environment.In all case time measurements were use to provide comparisons between manual and automated processing of typical radiotherapy planning tasks. ResultsContouring of DICE scores showed strong agreement (over 0.90) for the vast majority of regions of interest, with an average DICE coefficient of 77.7 for breast patients and 81.8 for prostate.Results were improved with post processing.Breast and prostate plans show comparable plan quality with manual planning for both simple single phase and advanced 3-4 target volume techniques with dose volume histogram differences consistently within 5% TD Point to point comparisons between automatic deformation matches and manual user deformations showed varying results highlighting the current need for visual QA of deformable registrations.Time improvements over manual processes are recoreded for both breast and prostate patients in all areas of testing Conclusion The possibility of automation to provide efficiency and consistency on a departmental and larger scale is demonstrated.The work represents a step in the correct direction rather than a finished produce to radiotherapy automation and the current limitations and problems are opened to the audience for responses and questions.