ArticleFrontiers in oncology2026
Economics achieved through the use of artificial intelligence-powered contouring solutions in a network of oncology clinics in low- and middle-income countries.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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Abstract
Background: Contouring of target volumes and organs-at-risk (OARs) is among the most time-intensive steps in radiation therapy planning. Artificial intelligence (AI)-based autocontouring has the potential to improve efficiency and reduce clinician workload, particularly in high-volume settings. Methods: This multi-center study evaluated time and labor cost savings following implementation of an AI-powered autocontouring solution (AI-Rad Companion Organs RT, Siemens Healthineers) across 18 oncology centers in a low- and middle-income country. Six physicians assessed 116 radiotherapy planning cases across multiple anatomical sites. Time required for manual contouring and post-autocontour editing was recorded. Time savings were converted into monetary value using three clinician cost scenarios, accounting for a notional software usage cost. Results: Mean time savings across all cases was 13.8 minutes. After accounting for software costs, net savings ranged from INR 37 to INR 670 per case depending on clinician cost assumptions. Conclusion: AI-based autocontouring significantly reduces clinician workload and delivers measurable labor cost savings in routine radiation oncology practice, supporting adoption in LMIC settings.
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