Health economic evaluation of artificial intelligence in dentistry

The health economic evidence on artificial intelligence in dentistry is thin, and this review maps what exists and what is missing. Direct cost-effectiveness evidence is confined to a small number of studies, chiefly a cost-effectiveness model and a randomized trial of artificial intelligence for caries detection, both of which find that the economic value depends on the clinician's baseline accuracy, on the cost of the software, and on how the detection changes treatment, not on the algorithm's accuracy alone. The evidence on workflow and time efficiency is more substantial but is rarely translated into monetary terms, and the evidence on implementation and infrastructure costs, on equity and access, and on reimbursement and adoption is almost entirely absent from the dental literature, so the economic case for dental artificial intelligence currently rests on analogy with medical imaging and on assumptions rather than on demonstrated value. The review organizes the evidence along five thematic axes, cost-effectiveness, efficiency, implementation cost, equity, and adoption, and concludes that the field's next step is not more accuracy studies but economic evaluations that measure what the technology costs and what it changes in the care pathway, reported to the standards that health economics has already established.