How Well Do AI-Enabled Decision Support Systems Perform in Clinical Settings?
Stud Health Technol Inform
; 310: 279-283, 2024 Jan 25.
Article
en En
| MEDLINE
| ID: mdl-38269809
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Sistemas de Apoyo a Decisiones Clínicas
/
Aprendizaje Automático
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
/
Systematic_reviews
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article
País de afiliación:
Australia