How threshold customisation affects the performance of a multiclass X-ray AI model for primary care triage: a retrospective study
Threshold optimisation is critical for adapting AI models to context-specific clinical workflows. Our study shows that adjusting the operating threshold enabled prioritisation of sensitivity and NPV, supporting safe AI-assisted triage in primary care. This is a deeply collaborative process that must involve radiology and clinical teams: selecting appropriate thresholds aligned with clinical objectives for safe and effective implementation. Future work will assess real-world operational impact and user acceptance following prospective deployment.
Authors
- Alvin Yong Quan Soon
- Jia Lu Lilian Lin (ORCID: https://orcid.org/0000-0003-0644-6399)
- Qi Wei Fong (ORCID: https://orcid.org/0000-0002-3424-8915)
- Jordan Sim (ORCID: https://orcid.org/0000-0002-4164-1643)
- Lay Wai Khin (ORCID: https://orcid.org/0000-0001-9181-0372)
- Cher Heng Tan
- Sreejith Balakrishnan
- Sabrina Wong
- Timothy Lin
Institutions
- Tan Tock Seng Hospital (SG)
- SingHealth Polyclinics (SG)
- Singapore-MIT Alliance for Research and Technology (SG)
Publication Details
- Journal
- BMJ Open
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1136/bmjopen-2025-111127
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Health Insurance Service