The Challenges of Implementing Artificial Intelligence into Surgical Practice.

Journal: World journal of surgery

PMID: 33051700

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).

Authors

Isaac Tranter-Entwistle

Department of General Surgery, Christchurch Hospital, Christchurch, New Zealand. tranter.isaac@gmail.com.
Holly Wang

Decision Support, Christchurch Hospital, Christchurch, New Zealand.
Kenny Daly

Decision Support, Christchurch Hospital, Christchurch, New Zealand.
Scott Maxwell

Decision Support, Christchurch Hospital, Christchurch, New Zealand.
Saxon Connor

Department of General Surgery, Christchurch Hospital, Christchurch, New Zealand.

Keywords

Acute Disease Adult Aged Aged, 80 and over Artificial Intelligence Biliary Tract Diseases Bilirubin Cholangiopancreatography, Endoscopic Retrograde Choledocholithiasis Computer Simulation Female Humans Liver Function Tests Machine Learning Male Middle Aged Predictive Value of Tests Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (33051700)

The Challenges of Implementing Artificial Intelligence into Surgical Practice.

Abstract

Authors

Keywords

External Resources

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