The Challenges of Implementing Artificial Intelligence into Surgical Practice

Abstract

Background Artificial intelligence is touted as the future of medicine. Classical algorithms for the detection of common bile duct stones (CBD) have had poor clinical uptake due to low accuracy. This study explores the challenges of developing and implementing a machine‐learning model for the prediction of CBD stones in patients presenting with acute biliary disease (ABD). Methods All patients presenting acutely to Christchurch Hospital over a two‐year period with ABD were retrospectively identified. Clinical data points including lab test results, demographics and ethnicity were recorded. Several statistical techniques were utilised to develop a machine‐learning model. Issues with data collection, quality, interpretation and barriers to implementation were identified and highlighted. Results Issues with patient identification, coding accuracy, and implementation were encountered. In total, 1315 patients met inclusion criteria. Incorrect international classification of disease 10 (ICD‐10) coding was noted in 36% (137/382) of patients recorded as having CBD stones. Patients with CBD stones were significantly older and had higher aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin and gamma‐glutamyl transferase (GGT) levels (p < 0.001). The no information rate was 81% (1070/1315 patients). The optimum model developed was the gradient boosted model with a PPV of 67%, NPV of 87%, sensitivity of 37% and a specificity of 96% for common bile duct stones. Conclusion This paper highlights the utility of machine learning in predicting CBD stones. Accuracy is limited by current data and issues do exist around both the ethics and practicality of implementation. Regardless, machine learning represents a promising new paradigm for surgical practice.

Standardizing process in acute biliary disease

“…Internal audit (unpublished data) identified that only 40% of MRCPs performed identified choledocholithiasis, while over 90% of patients would have been suitable for up front LC and IOC. Several preoperative decision scores are available to stratify the risk of CBD stones using a combination of LFTs and preoperative imaging 24,25 . Despite improved performance over ad hoc, clinical decision making their use in practice is not routine.…”

“…Several preoperative decision scores are available to stratify the risk of CBD stones using a combination of LFTs and preoperative imaging. 24,25 Despite improved performance over ad hoc, clinical decision making their use in practice is not routine. Initial LC for patients at intermediate risk of CBD stones has been shown to reduce LOS and cost without compromising patient outcomes.…”

Applications of artificial intelligence in pancreatic and biliary diseases

“…Tranter‐Entwistle et al . explored the potential usefulness of AI for predicting common bile duct stones in patients with acute biliary disease and achieved a sensitivity of 37% and a specificity of 96% based on the results of blood tests 49 . Notably, in that study, the authors described challenges posed by coding errors and missing data in real‐world clinical data, highlighting the hurdles of such research and the need for large datasets to achieve adequate model performance.…”

Challenges of Implementing Artificial Intelligence in Interventional Radiology

“…Multiple studies have briefly touched on some of the difficulties of implementing AI in procedural specialties and practices. 10,13,17,20,39,40 Here, we break down the most significant of these challenges in the following categories: inherent specialty challenges, regulatory challenges, intellectual property, raising capital, and ethical challenges.…”