INTRODUCTION:
Artificial intelligence–assisted chest radiography has demonstrated potential for detecting pulmonary abnormalities, including incidental pulmonary nodules. In resource-constrained healthcare systems, chest radiography is often discussed as a pragmatic component of emerging lung cancer screening pathways, despite low-dose computed tomography being the diagnostic standard. However, evidence on the feasibility and acceptance of artificial intelligence–assisted chest radiography among healthcare providers within real-world clinical environments remains limited.

METHODS:
A qualitative exploratory study was conducted using a focus group discussion involving radiologists and radiographers from public and university hospitals in Malaysia. Data were analysed using a hybrid deductive–inductive thematic analysis approach. The Technology Acceptance Model was applied as a sensitising framework to examine perceived usefulness, ease of use, and contextual factors influencing acceptance, while allowing themes to emerge inductively from the data.

RESULTS:
Four interrelated themes were identified. Perceived clinical value reflected the potential of artificial intelligence–assisted chest radiography to support early detection and triage of incidental pulmonary nodules, particularly in settings where access to advanced imaging is constrained. Workflow integration and usability challenges highlighted the importance of seamless system integration to minimise additional workforce burden. Trust, interpretation, and professional responsibility emphasised conditional acceptance, with concerns regarding false positives, interpretive literacy, and the need for human oversight. Organisational readiness and enabling conditions underscored the role of leadership support, information technology infrastructure, governance, and training in determining feasibility.

CONCLUSIONS:
Healthcare providers’ acceptance of artificial intelligence–assisted chest radiography for incidental pulmonary nodule detection is conditional and shaped by clinical value, workflow compatibility, professional trust, and organisational readiness. When positioned within emerging lung cancer screening pathways, artificial intelligence–assisted chest radiography may serve as a pragmatic triage tool rather than a replacement for low-dose computed tomography, highlighting the need for a socio-technical approach to implementation.