The impact of artificial intelligence on the cost of radiotherapy in low- and middle-income countries
This master’s thesis explores how artificial intelligence (AI) can help address economic
and operational barriers to radiotherapy in low and middle income countries (LMICs),
where access is limited despite a rising cancer burden. A major obstacle in these regions is the shortage of trained professionals and the high cost of treatment planning
and delivery.
The study evaluates whether the Radiotherapy Planning Assistant (RPA), an AI based
tool, can improve efficiency and reduce costs in LMIC radiotherapy departments. Data
from four centres in the ARCHERY study,covering cervical, head and neck, and prostate
cancers, were analysed using the ESTRO HERO Time Driven Activity Based Costing
(TDABC) model. Simulations modelled planning time reductions of 70 percent, 80 percent, and 90 percent for eligible tumour types.
Baseline results showed equipment as the dominant cost driver, limiting overall cost
reductions. However, time savings from AI integration improved treatment planning
system availability and reduced staff workload. These gains suggest enhanced efficiency and capacity, especially in high volume settings. While AI may not yield large
financial savings alone, it alleviates key bottlenecks and supports workforce optimisation, provided infrastructure and system capacity are strengthened.
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