Health economic assessment on AI cancer prediction platform in the UK National Health Service.

32 Background: Cancer care imposes substantial financial burdens on healthcare systems, with late stage diagnosis and urgent suspected cancer (USC) referrals contributing significantly to costs. The COVID-19 pandemic has further strained resources, complicating elective care recovery efforts. Artificial intelligence (AI) platforms, such as C the Signs , offer potential to enhance care quality while reducing expenditure. Methods: An economic model was developed incorporating published impact data from C the Signs , focusing on metrics including cancer detection rates, conversion rates, and the platform's capacity to triage patients away from USC pathways. The model utilized cost differentials between early and late-stage cancer treatments and calculated clinician time savings from avoided USC referrals. The analysis evaluated the prospective impact on the Suffolk and North East Essex Integrated Care Board (ICB), serving a population of 1.1 million, where C the Signs has been implemented since November 2023. Results: The model projected annual savings of £1.5 million, yielding an 808% return on investment. Implementation of C the Signs was associated with 272 patients diagnosed earlier and the avoidance of 12,374 USC referrals, translating to a saving of 4,125 clinician hours annually. Conclusions: The integration of C the Signs within the NHS demonstrates significant potential for cost savings and clinician time efficiency, offering a high return on investment. These findings support the adoption of AI-driven tools to optimize cancer care delivery and resource utilization.