Publikasi Scopus 2024 per tanggal 30 Juni 2024 (499 artikel)

Susanto A.P.; Lyell D.; Widyantoro B.; Berkovsky S.; Magrabi F.
Susanto, Anindya Pradipta (57221504275); Lyell, David (57193603939); Widyantoro, Bambang (35286148600); Berkovsky, Shlomo (8945336100); Magrabi, Farah (6602783750)
57221504275; 57193603939; 35286148600; 8945336100; 6602783750
How Well Do AI-Enabled Decision Support Systems Perform in Clinical Settings?
2024
Studies in Health Technology and Informatics
310
279
283
4
0
Australian Institute of Health Innovation, Macquarie University, Australia; Faculty of Medicine, Universitas Indonesia, Indonesia
Susanto A.P., Australian Institute of Health Innovation, Macquarie University, Australia, Faculty of Medicine, Universitas Indonesia, Indonesia; Lyell D., Australian Institute of Health Innovation, Macquarie University, Australia; Widyantoro B., Faculty of Medicine, Universitas Indonesia, Indonesia; Berkovsky S., Australian Institute of Health Innovation, Macquarie University, Australia; Magrabi F., Australian Institute of Health Innovation, Macquarie University, Australia
Real-world performance of machine learning (ML) models is crucial for safely and effectively embedding them into clinical decision support (CDS) systems. We examined evidence about the performance of contemporary ML-based CDS in clinical settings. A systematic search of four bibliographic databases identified 32 studies over a 5-year period. The CDS task, ML type, ML method and real-world performance was extracted and analysed. Most ML-based CDS supported image recognition and interpretation (n=12; 38%) and risk assessment (n=9; 28%). The majority used supervised learning (n=28; 88%) to train random forests (n=7; 22%) and convolutional neural networks (n=7; 22%). Only 12 studies reported real-world performance using heterogenous metrics; and performance degraded in clinical settings compared to model validation. The reporting of model performance is fundamental to ensuring safe and effective use of ML-based CDS in clinical settings. There remain opportunities to improve reporting. © 2024 International Medical Informatics Association (IMIA) and IOS Press.
Clinical decision support; machine learning; performance
Benchmarking; Databases, Bibliographic; Decision Support Systems, Clinical; Machine Learning; Neural Networks, Computer; Decision support systems; Forestry; Image recognition; Information services; Medical informatics; Risk assessment; Bibliographic database; Clinical decision support; Clinical decision support systems; Clinical settings; Embeddings; Machine learning models; Machine-learning; Performance; Real-world performance; Systematic searches; benchmarking; clinical decision support system; conference paper; convolutional neural network; decision support system; drug concentration; female; human; learning; machine learning; male; medical informatics; medical information system; random forest; risk assessment; systematic review; artificial neural network; benchmarking; bibliographic d
IOS Press BV
09269630
978-164368456-7
38269809
Conference paper
Q3
285
15637