首页|University of Nottingham Reports Findings in Artificial Intelligence (Cost-Effec tiveness of AI for Risk-Stratified Breast Cancer Screening)
University of Nottingham Reports Findings in Artificial Intelligence (Cost-Effec tiveness of AI for Risk-Stratified Breast Cancer Screening)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Nottingham, Unite d Kingdom, by NewsRx journalists, research stated, “Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) ris k prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains.”The news correspondents obtained a quote from the research from the University o f Nottingham, “To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program. This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to est imate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 ye ars at screening. Mammography screening at 1 to 6 yearly screening intervals bas ed on breast cancer risk and standard care (screening every 3 years). Incrementa l net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28). Artificial intelligence-based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screeni ng program. A screening schedule of every 6 years for lowest-risk individuals, b iannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (U S $1.28) was estimated to generate a yearly monetary benefit of app roximately £10.6 (US $13.6) million. In this decision analytical mo del study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced cost s. These results are particularly relevant for health care settings where resour ces are under pressure.”
NottinghamUnited KingdomEuropeArti ficial IntelligenceBreast CancerBreast Cancer ScreeningCancerDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineMachine LearningOn cologyRisk and PreventionWomen’s Health
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