Abstract
Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.
| Original language | English |
|---|---|
| Article number | e100971 |
| Pages (from-to) | 1-6 |
| Number of pages | 6 |
| Journal | BMJ Health and Care Informatics |
| Volume | 31 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 30 May 2024 |
Bibliographical note
Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Artificial intelligence
- Decision Making, Computer-Assisted
- Deep Learning
- Machine Learning
- Medical Informatics Applications