Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive diagnostic procedures

Abstract

Literature review: Cystoscopy is the gold standard for initial macroscopic assessments of the human urinary bladder to rule out (or diagnose) bladder cancer (BCa). Despite having guidelines, cystoscopic findings are diverse and often challenging to classify. The extent of the false negatives and false positives in cystoscopic diagnosis is currently unknown. We suspect that there is a certain degree of under-diagnosis (like the failure to detect malignant tumours) and over-diagnosis (e.g. sending the patient for unnecessary transurethral resection of bladder tumors with anesthesia) that put the patient at risk. Conclusions: XAI robot-assisted cystoscopes would help to overcome the risks/flaws of conventional cystoscopy. Cystoscopy is considered a less life-threatening starting point for automation than open surgical procedures. Semi-autonomous cystoscopy requires standards and cystoscopy is a good procedure to establish a model that can then be exported/copied to other procedures of endoscopy and surgery. Standards also define the automation levels—an issue for medical product law. These cystoscopy skills do not give full autonomy to the machine, and represent a surgical parallel to ‘Autonomous Driving’ (where a standard requires a human supervisor to remain in the ‘vehicle’). Here in robotic cystoscopy, a human supervisor remains bedside in the ‘operating room’ as a ‘human‐in‐the‐loop’ in order to safeguard patients. The urologists will be able to delegate personal- and time-consuming cystoscopy to a specialised nurse. The result of automated diagnostic cystoscopy is a short video (with pre-processed photos from the video), which are then reviewed by the urologists at a more convenient time.