The PERFORMS self-assessment scheme measures individuals skills in identifying key mammographic features on sets of known cases. One aspect of this is that it allows radiologists' skills to be trained, based on their data from this scheme. Consequently, a new strategy is introduced to provide revision training based on mammographic features that the radiologist has had difficulty with in these sets. To do this requires a lot of random cases to provide dynamic, unique,
and up-to-date training modules for each individual. We propose GIMI (Generic Infrastructure in Medical Informatics) middleware as the solution to harvest cases from distributed grid servers. The GIMI middleware enables existing and legacy data to support healthcare delivery, research, and training. It is technology-agnostic,
data-agnostic, and has a security policy. The trainee examines each case, indicating the location of regions of interest, and completes an
evaluation form, to determine mammographic feature labelling, diagnosis, and decisions. For feedback, the trainee can
choose to have immediate feedback after examining each case or batch feedback after examining a number of cases. All
the trainees' result are recorded in a database which also contains their trainee profile. A full report can be prepared for
the trainee after they have completed their training. This project demonstrates the practicality of a grid-based individualised training strategy and the efficacy in generating dynamic training modules within the coverage/outreach of the GIMI middleware. The advantages and limitations of the approach are discussed together with future plans.