Computer-aided diagnosis of lower abdominal pain in women

Limits to diagnostic accuracy

This paper explores the limits to computer-aided medical diagnosis. A specific application area (the diagnosis of abdominal pain of suspected gynaecological origin) is chosen, and the factors limiting the accuracy of computer programs are investigated by means of a simulation model which has been shown previously to generate realistic cases. The model is used to generate arbitrarily large training and test sets. The results suggest that, while statistical dependencies exist amongst symptoms and signs, there is little to be gained by taking interactions into account. However, failure to record all possible observations does limit diagnostic accuracy significantly. The results suggest that near-optimal diagnostic accuracy (75-80%) can be obtained with a training set size of 10(5) cases simply by applying Bayes' theorem with the usual assumption of conditional independence.

Computer-aided decision support--glittering prospects, practical problems, and Pandora's box

This chapter explores some of the prospects for and problems of computer-aided decision support in clinical medicine, with special reference to acute abdominal pain of both surgical and gynaecological origin. The need for such systems is argued, and it is further demonstrated that their provision is both feasible and (where implemented) associated with tangible improvements in performance by inexperienced hospital doctors. The problems of implementation are nevertheless formidable and these are discussed.

Computer-aided diagnosis: a review

Fifteen years ago, computer-aided diagnosis of the acute abdomen promised much. Today it is little used. Studies have been flowed by poor trial design, bias, selective reporting of results, statistical naivety and spurious conclusions. The computer system lacks 'common sense' and is less accurate than clinicians. Yet its introduction has been associated with improved patient management and outcome. Much of the effect arises from structured data collection methods and some from audit feedback to clinicians. It is on these innovations, not on the computer, that future work should focus.