Socioeconomic assessment of investments in dental anthropomorphic robots for practical training of dentistry students

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INTRODUCTION

In recent years, the robotics industry has been experiencing rapid growth, including in the field of medical education. This trend reflects a drive to optimize training processes and enhance the quality of professional education [1]. Advanced technologies, including artificial intelligence and high-precision mechanisms, facilitate the integration of robots into the training of future healthcare professionals [2]. Robots have proven to be effective in dental education, helping students practice clinical skills, deepen theoretical knowledge, and improve nonverbal communication, an essential component of successful clinical practice [3, 4].

Modern technological solutions have enabled the development of training systems that promote comprehensive skill development, addressing the limitations of current practice-based education in both undergraduate and postgraduate medical institutions [5]. Conventional dental simulators, which are static mannequins, fail to replicate a real-world clinical setting and do not offer immersive patient care experiences [6, 7]. This limitation contributes to a gap between theoretical instruction and clinical proficiency. In contrast, educational robots capable of simulating patient behavior and offering interactive feedback enhance engagement and support deeper learning [8, 9] (Fig. 1).

 

Fig. 1. Dental anthropomorphic robot.

 

Given the high cost of developing and implementing such high-tech devices, a detailed economic evaluation is warranted. This allows institutions to assess the feasibility and potential benefits of adopting these tools and their impact on educational outcomes. The present study examines a case of practice-based training using Russian full-function dental anthropomorphic robots (DARs) [10–14].

AIM: To evaluate the economic feasibility of investing in DARs for practice-based training of dentistry students.

METHODS

Case Model

This study assessed the economic feasibility of incorporating DARs into the clinical training curriculum of a medical university. The analysis focused on evaluating the costs and benefits of integrating DARs into the educational process to enhance student motivation, training quality, and the level of dental care.

Training Parameters and Costs

The primary parameter used to evaluate cost-effectiveness was the tuition cost per student, which serves as a proxy for assessing the financial efficiency of implementing DARs. The tuition fee for the Dentistry program at the Russian University of Medicine is 500,000 rubles per year for a five-year curriculum, equivalent to 1,100 hours annually. This results in an hourly training cost of 455 rubles.

To assess implementation costs, the capital expense of a single DAR and its annual maintenance were analyzed. The robot, designed to substantially improve students’ clinical competencies, costs 12,000,000 rubles. Annual maintenance was estimated at 10% of the robot’s cost (1,200,000 rubles), based on reference data for high-tech equipment servicing [15].

A 12-year lifespan was adopted for long-term analysis of the costs and benefits associated with DAR implementation.

Training Process

For the economic analysis, it was assumed that students would train with the robot Monday through Friday, from 9:00 AM to 9:00 PM, totaling 12 hours per day, or 2,880 hours annually. Two educational models were considered: group training involving 10 students supervised by an instructor and one-on-one training sessions (Fig. 2).

 

Fig. 2. Clinical training using a dental anthropomorphic robot.

 

Economic Indicators

To assess the cost-effectiveness of using the robot, the following financial metrics were applied:

Net present value (NPV): This metric represents the difference between the present value of projected cash flows and the total investment and maintenance costs. A positive NPV indicates that the project generates more value than it costs and is therefore considered profitable. A negative NPV suggests a financially unviable project. The NPV was calculated using the following formula:

$\mathrm{NVP}=\sum _1^n\frac{P}{{\left(1+R\right)}^n}-I-\sum _1^n\frac{C}{{\left(1+R\right)}^n},$

where P is the cash flow, R is the discount rate, n is the time period, I is the initial investment, and C is the maintenance cost.

Profitability index (PI), also known as the benefit-cost ratio, serves as a supplement to NPV, offering insight into the efficiency of the investment. PI is expressed as a ratio of the present value of future cash flows to the initial investment.

Based on the PI value, the project’s feasibility can be interpreted as follows:

• PI > 1: the project is considered profitable and viable for implementation;
• PI = 1: the project requires further evaluation using additional efficiency metrics to determine feasibility;
• PI < 1: the project is deemed unprofitable and should not be pursued.

The PI was calculated using the following formula:

$\mathrm{PI}=1+\frac{\mathrm{NPV}}{\mathrm{IC}}$,

where NPV is the net present value, and IC is the initial investment.

Discount rate (R) was set to match the risk-free rate of Russian government bonds in 2023, which was 15.09%.

Internal rate of return (IRR) is the discount rate at which the NPV of future cash flows equals the initial investment, indicating the breakeven point.

The IRR was calculated using the following formula:

$\sum _1^n\frac{P}{{\left(1+IRR\right)}^n}-I=0$, или $\sum _1^n\frac{P}{{\left(1+IRR\right)}^n}=I$,

where P is the cash flow, n is the time period, and I is the initial investment.

The payback period (PP) was calculated using the following formula:

$\mathrm{PP}=\frac{\mathrm{IC}}{P}$,

where IC is the initial investment, and P is the annual cash flow.

The discounted payback period (DPP) was calculated using the following formula:

DPP=min(n), $\sum _1^n\frac{P}{{\left(1+R\right)}^n}\ge I$,

where P is the cash flow, R is the discount rate, and I is the investment.

RESULTS

The economic evaluation of investments in the use of DARs for practice-based training of dental students is presented in Table 1.

 

Table 1. Economic evaluation of investment in a dental anthropomorphic robot

Indicator	1 robot per 10 students	1 robot per student
Net present value	54,279,963 rubles	59,403,855 rubles
Profitability index	5.52	0.22
Internal rate of return	70.79%	–4.24%
Payback period	1 year	9 years
Discounted payback period	2 years	12 years

 

Based on the reported economic indicators, the following conclusions can be drawn about the profitability of investing in DARs for practice-based training of dental students:

1. NPV = 54,279,963 rubles;
2. PI = 5.52;
3. IRR = 70.79%;
4. PP = 1 year; DPP = 2 years.

DISCUSSION

Our findings indicate that investments in DARs for practice-based training of dentistry students are highly cost-effective. The project demonstrated a substantial positive NPV, a high PI, and an IRR significantly above typical discount rates, all coupled with a short payback period. These results validate the economic feasibility and attractiveness of the investment.

For the scenario involving one robot per 10 students, the NPV was 54,279,963 rubles. This positive value confirms that the project generates income exceeding both the initial and ongoing costs, underlining its profitability. This outcome can be attributed to efficient time utilization, as the robot ensures continuous training and access to hands-on skill development.

The PI of 5.52 implies that for every ruble invested, the project returns 5.52 rubles of discounted income. A PI > 1 affirms the financial benefit and rationality of the investment, reflecting its strong return, investor appeal, resilience to economic fluctuations, and optimal allocation of educational resources.

An IRR of 70.79% substantially exceeds standard discount rates, underscoring the project’s robustness and ability to generate stable cash flow even amid potential economic instability. This also explains the rapid investment recovery (PP≈1 year, DPP=2 years).

Although current sources lack direct data on the economic efficiency of anthropomorphic robots in medical education, trends in the robotics economy suggest significant potential. For example, Arduengo et al. [14] note that robotics adoption enhances productivity and reduces labor costs associated with routine tasks. Similar transitions have been observed in various economic sectors, where automation has minimized labor costs and reallocated human resources to more skilled roles.

These principles can be extrapolated to education, where DARs can lessen instructor workload and improve cost efficiency by optimizing the teaching process.

A systematic review by Turchetti et al. showed that robotic systems such as the da Vinci surgical robot entail high acquisition and maintenance costs, rendering robotic procedures more expensive than traditional methods. However, as users gain experience, procedure times and costs tend to decrease [15]. Therefore, despite high upfront investment, long-term benefits from automation in precision-demanding environments can outweigh the initial costs, a notion applicable to medical education.

Ghani et al. explored various facets of using robots in education, highlighting both advantages and limitations. They concluded that, despite the optimization potential, constraints in social interaction and adaptive response to individual learners may limit the effectiveness of robotic systems. This underscores the need for careful integration of robotics in educational settings, especially in disciplines requiring interpersonal engagement [16].

Hence, the economic potential of using robots in education should be considered alongside their advantages and limitations, particularly in medical education.

Study limitations. This study used data from a single institution, the Russian University of Medicine, which may limit the applicability of the findings to other medical schools with different infrastructures and resources. Further research across multiple institutions is necessary to validate these findings.

Parameters such as robot maintenance costs and service life were estimated, which may impact the long-term economic feasibility. Future studies should consider cost fluctuations and develop scenarios under various economic conditions.

While standard financial indicators (NPV, PI, IRR, PP, DPP) are widely accepted for investment evaluation, they may not fully capture the educational value and long-term benefits, such as improved training quality and enhanced clinical competencies.

This analysis focused on a mid-term horizon (up to 12 years). Future research should also assess the long-term effects of robotic integration on students’ professional growth.

CONCLUSION

The study confirms the high economic and educational efficiency of using DARs in the training of dental students. The project demonstrates favorable financial metrics, including a positive NPV and high PI and IRR values, indicating its profitability. Additionally, robotic use enhances practical skills such as procedural accuracy and speed, as reflected in student assessments and instructor feedback.

ADDITIONAL INFORMATION

Funding sources: This study was supported by the Perm Scientific and Educational Center “Rational Subsoil Use” (project “New Materials and Technologies for Medicine”, 2024).

Disclosure of interests: The authors declare no explicit or potential conflicts of interests associated with the publication of this article.

Author contributions: All authors confirm that their authorship meets the international ICMJE criteria (all authors made substantial contributions to the conceptualization, investigation, and manuscript preparation, and reviewed and approved the final version prior to publication). The authors made the following contributions: O.A. Tsarevskiy: economic assessment, data collection and analysis, writing; S.A. Arutyunov: economic analysis, editing; D.I. Grachev: review, conceptualization, writing; N.B. Astashina: published data collection and analysis, writing; K.G. Akhmedov: data analysis, writing; A.A. Uzhakov: technical assessment, writing and editing.

About the authors

Oleg A. Tsarevskiy

Bauman Moscow State Technical University

Author for correspondence.
Email: t1sarevskiy@yandex.ru
ORCID iD: 0009-0000-8602-6305
SPIN-code: 7244-1376
Russian Federation, 5 2nd Baumanskaya street, 105005 Moscow

Dmitriy I. Grachev

Russian University of Medicine

Email: dr.grachev@mail.ru
ORCID iD: 0000-0002-5758-7485
SPIN-code: 2307-4380

MD, Cand. Sci. (Medicine), Associate Professor

Russian Federation, Moscow

Alexander A. Yuzhakov

Perm National Research Polytechnic University

Email: uz@at.pstu.ru
ORCID iD: 0000-0003-1865-2448
SPIN-code: 4820-8360

Dr. Sci. (Engineering), Professor

Russian Federation, Perm

Nataliya B. Astashina

Academician Ye.A. Vagner Perm State Medical University

Email: astashina.nb@mail.ru
ORCID iD: 0000-0003-1135-7833
SPIN-code: 6119-8171

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Perm

Kamalutdin G. Akhmedov

Russian University of Medicine

Email: kama05doc@gmail.com
ORCID iD: 0009-0000-5195-3942
SPIN-code: 8229-7763
Russian Federation, Moscow

Sergey A. Arutyunov

Russian University of Medicine

Email: sa.arutyunov@mail.ru
ORCID iD: 0009-0005-7605-5715
SPIN-code: 4682-1730
Russian Federation, Moscow

References

Supplementary files