Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
BMJ open ophthalmology
Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis
OBJECTIVE: Cost analysis of autonomous artificial intelligence (AI)-based screening of diabetic retinopathy (DR) for adults with diabetes at a primary care clinic. METHODS AND ANALYSIS: This study provides a comparative cost analysis of actual results using AI-based DR screening with counterfactual results based on all patients going through the physician-based referral system. A cost analysis is conducted using cost data from published sources, provincial billing codes, statistical sources, and patient characteristics from a clinical study to compare autonomous AI-based screening for DR versus physician-based screening. Costs considered include direct costs of operating the AI system, physician fees, and indirect costs to patient time. Along with total cost comparisons, a cost per DR case detected is estimated and a sensitivity analysis based on variations in AI costs is provided. RESULTS: Over the study period, 202 participants were screened for DR using autonomous AI. The majority (93.6%, n=189) of AI-based DR screening exams were completed successfully. The AI-based scenario results in total direct costs of $C7919.04 and indirect costs of $C5728.80, resulting in total costs of $C13 647.84 per 100 patients. The traditional physician-based approach results in total direct costs of $C8240 and indirect costs of $C19 998.09, resulting in total costs of $C28 238.09 for 100 patients. When costs are converted to costs per unit outcome, the total cost per diagnosed DR case is $C620.36 for the AI-based approach and $C1283.55 for the physician-based approach; the AI-based cost per diagnosed case was 52% lower. CONCLUSION: Given the lower cost per diagnosed case of the AI-based approach, there are advantages to the implementation of AI-based screening for DR.
1 Aug 2026
Read appraisal →Journal of medical ethics
AI interventions in cancer screening: balancing equity and cost-effectiveness
This paper examines the integration of artificial intelligence (AI) into cancer screening programmes, focusing on the associated equity challenges and resource allocation implications. While AI technologies promise significant benefits-such as improved diagnostic accuracy, shorter waiting times, reduced reliance on radiographers, and overall productivity gains and cost-effectiveness-current interventions disproportionately favour those already engaged in screening. This neglect of non-attenders, who face the worst cancer outcomes, exacerbates existing health disparities and undermines the core objectives of screening programmes.Using breast cancer screening as a case study, we argue that AI interventions must not only improve health outcomes and demonstrate cost-effectiveness but also address inequities by prioritising non-attenders. To this end, we advocate for the design and implementation of cost-saving AI interventions. Such interventions could enable reinvestment into strategies specifically aimed at increasing engagement among non-attenders, thereby reducing disparities in cancer outcomes. Decision modelling is presented as a practical method to identify and evaluate these cost-saving interventions. Furthermore, the paper calls for greater transparency in decision-making, urging policymakers to explicitly account for the equity implications and opportunity costs associated with AI investments. Only then will they be able to balance the promise of technological innovation with the ethical imperative to improve health outcomes for all, particularly underserved populations. Methods such as distributional cost-effectiveness analysis are recommended to quantify and address disparities, ensuring more equitable healthcare delivery.
25 July 2026
Read appraisal →International journal of technology assessment in health care
Consideration of intersectoral costs and benefits in health economic evaluations of pneumococcal conjugate vaccines: a systematic review.
OBJECTIVES: This systematic review explores which intersectoral costs and benefits (ICBs) are considered in the economic evaluation of pneumococcal conjugate vaccines (PCVs) across different age groups and the related impacts on the results of economic evaluations. METHODS: Seven databases were searched from 2009 to 2024 for full economic evaluations (Ees) of PCVs from a societal perspective. ICBs were presented narratively and in tabular form. Studies were appraised on quality, and PRISMA guidelines were followed. RESULTS: In all, 57 studies were included. ICBs were captured in the following sectors: patient and family, paid labor (productivity), nonpaid productivity, other sectors (education, leisure, consumption), unspecified sectors for ecological effects (herd immunity, serotype replacement, cross-protection), and equity. In cases where the comparator was no vaccination, 10 studies reported that PCVs were dominant when ICBs were included. After excluding dominant analyses, the median QALY-based incremental cost-effectiveness ratio (ICER) reduction ranged from -3.25 percent (for patient/family costs and productivity loss) to -46.02 percent (in children with ecological effects). Among DALY-based studies, the inclusion of ecological effects reduced the median ICER by -22.97 percent. In head-to-head comparisons, 16 studies reported that PCVs were dominant when ICBs were included. CONCLUSION: Inclusion of ICBs, except for serotype replacement, showed favorable cost-effectiveness results. This study provides a framework for identifying the intersectoral costs and benefits for the health economic evaluation of PCVs in children and adults.
19 July 2026
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