A typical radiology department is a radiological pathology banquet, with more food than the radiologist in training (ie all radiologists) can consume. The problem is that the cases are hidden - time constraints and diversions prevent the systematic logging of teaching cases and one remains unaware of great cases seen by colleagues.

The proportion of studies that are abnormal has fallen in advanced imaging modalities over recent decades, reflecting a more investigation-led style of medicine, and the wider availability of imaging. The trainee is faced with a double whammy of more studies and more refined diagnoses than ever before, more diluted with normals.

A decade ago a trainee could wait to catch pathology as it passed by. Today a more active search is needed. AutoTeaCH seeks to alleviate this problem. It takes as its input spreadsheets of reports that can be exported from radiology information (RIS) systems, and applies some natural language processing (NLP) to categorise and sort by diagnosis.

This yields lists of cases expressing the belief of the study reporter at the time, clearly not a gold standard diagnosis, and finds many more cases than are available by more traditional means. This is a good thing - it develops critical thinking in the 'dirty' real world and exposes the learner to the range of appearances for the diagnoses both typical and atypical - thereby avoiding the selection bias present in curated case archives.

We anticipate that the system will be of use at trainee and consultant level. A consultant who wants to broaden his range of reporting can see how relevant studies are reported by colleagues in his/her institution. An institution can gain insight into the pathology mix coming through the department. To facilitate these options we provide a user interface for the viewing of studies separately, but also the ability to download the extracted cases in bulk on a csv spreadsheet.