Abstract
<title>Abstract</title> <p>Lung cancer is a leading cause of ill health and death with early diagnosis key to improving outcomes. The LungIMPACT randomised study found that the use of an artificial intelligence (AI) tool to flag and prioritise chest X-rays (CXR) referred from primary care in the human reporter’s worklist, did not reduce time from CXR to lung cancer (LC) diagnosis. This health economic analysis estimated relative cost-effectiveness of using AI for CXR prioritisation, compared firstly to AI support in interpretation only and, secondly, to current practice using no AI support. The primary analysis estimated incremental cost per day reduction in time from CXR to lung cancer (LC) diagnosis or date of discharge from lung cancer pathway, of AI with vs without prioritisation. Secondary analyses addressed (i) AI with prioritisation vs AI without; (ii) AI with prioritisation vs No AI; and (iii) AI with or without prioritisation vs No AI. AI with prioritisation was not cost-effective vs. AI without prioritisation and secondary analysis showed that no AI was always more cost-effective. Budget impact was £2 million per year in England for AI with prioritisation vs AI without, rising to £16–35 million per year for comparisons to No AI. The results of this analysis show that AI prioritisation of GP-referred CXRs costs the NHS money and evidence must be generated for meaningful impact on the clinical pathway if AI is to be deployed in this setting.</p>