Artificial intelligence (AI) can assist dentists in image assessment, for example, caries detection. The wider health and cost impact of employing AI for dental diagnostics has not yet been evaluated. We compared the cost-effectiveness of proximal caries detection on bitewing radiographs with versus without AI. U-Net, a fully convolutional neural network, had been trained, validated, and tested on 3,293, 252, and 141 bitewing radiographs, respectively, on which 4 experienced dentists had marked carious lesions (reference test). Lesions were stratified for initial lesions (E1/E2/D1, presumed noncavitated, receiving caries infiltration if detected) and advanced lesions (D2/D3, presumed cavitated, receiving restorative care if detected). A Markov model was used to simulate the consequences of true- and false-positive and true- and false-negative detections, as well as the subsequent decisions over the lifetime of patients. A German mixed-payers perspective was adopted. Our health outcome was tooth retention years. Costs were measured in 2020 euro. Monte-Carlo microsimulations and univariate and probabilistic sensitivity analyses were conducted. The incremental cost-effectiveness ratio (ICER) and the cost-effectiveness acceptability at different willingness-to-pay thresholds were quantified. AI showed an accuracy of 0.80; dentists’ mean accuracy was significantly lower at 0.71 (minimum–maximum: 0.61–0.78, P < 0.05). AI was significantly more sensitive than dentists (0.75 vs. 0.36 [0.19–0.65]; P = 0.006), while its specificity was not significantly lower (0.83 vs. 0.91 [0.69–0.98]; P > 0.05). In the base-case scenario, AI was more effective (tooth retention for a mean 64 [2.5%–97.5%: 61–65] y) and less costly (298 [244–367] euro) than assessment without AI (62 [59–64] y; 322 [257–394] euro). The ICER was −13.9 euro/y (i.e., AI saved money at higher effectiveness). In the majority (>77%) of all cases, AI was less costly and more effective. Applying AI for caries detection is likely to be cost-effective, mainly as fewer lesions remain undetected. Notably, this cost-effectiveness requires dentists to manage detected early lesions nonrestoratively.
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:To determine the clinical and cost-effectiveness of interventions (non-selective, selective or stepwise carious tissue removal, sealing of carious lesions using sealant materials or preformed metal crowns, or NRCC) to treat carious lesions conventionally considered to require restorations (cavitated or micro-cavitated lesions, or occlusal lesions that are clinically non-cavitated but clinically/radiographically extend into dentine) in primary or permanent teeth with vital (sensitive) pulps.
With growing awareness of the large burden of oral diseases and how limited coverage affects both access and affordability, oral health policy has been receiving increased attention in recent years. This culminated in the adoption of the WHO resolution on Oral Health in 2021, which urges Member States to better integrate oral health into their universal health coverage and noncommunicable disease agendas. This study investigates major patterns and developments in oral health status, financing, coverage, access, and service provision of oral health care in 31 European countries. While most countries cover oral health care for vulnerable population groups, the level of statutory coverage varies widely across Europe resulting in different coverage and financing schemes for the adult population. On average, one third of dental care spending is borne by public sources and the remaining part is paid out-of-pocket or by voluntary health insurance. This has important ramifications for financial protection and access to care, leaving many dental problems untreated. Overall, unmet needs for dental care are higher than for other types of care and particularly affect low-income groups. Dental care is undergoing various structural changes. The number of dentists is increasing, and the composition of the health workforce is starting to change in many countries. Dental care is increasingly provided in group practices and by practices that are part of private equity firms. Although there are early signs of a shift towards more prevention of oral diseases, dental care overall remains focused on treatment. A lack of data affects all areas of oral health care and impede to inform policy-making on the underlying causes and the prevalence of oral disease, as well as the effectiveness of community preventive activities and oral health services.
Key Points Question Are existing artificial intelligence (AI) algorithms cost-effective for use as a decision-support system in dermatology, dentistry, and ophthalmology? Findings In this economic evaluation analyzing data from 3 Markov models used in previous cost-effectiveness studies, the use of AI was associated with a modest improvement in outcomes. All benefits were highly dependent on treatment effects assumed after diagnosis and were very sensitive to the fee paid for the use of AI. Meaning These results suggest that even when AI can achieve better diagnostic capacities than the average physician, this may not directly translate to better or cheaper care, and that analysis using this technology should be used on a case-by-case basis.
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