Evaluating the Impact of Digital Tool Utilization in Dentistry on Burnout Syndrome Among Dentists: An Entropy Analysis and AI-Driven Approach

Abstract

In the high-pressure environment of dental practice, dentistry burnout syndrome frequently manifests as emotional exhaustion, depersonalization, and reduced professional fulfillment. While traditional methods for assessing dentistry burnout syndrome often overlook the complex dynamics of stress factors, this study specifically aims to predict burnout syndrome utilizing entropy and artificial intelligence to verify whether digital tools can alleviate burnout levels among dental professionals. The methodology used incorporates ideas from thermodynamics to facilitate reasoning and data representation. Data were obtained through a questionnaire exploring four key areas, which integrated job satisfaction, artificial intelligence-powered tools, time and communication, and patient expectations. The cohort included 126 dental professionals aged 25 to 65, with a mean age of 39.2 ± 9.5, comprising both genders. An artificial neural network model is proposed, delivering an accuracy greater than 85% to predict the impact of digital tools on dentistry burnout syndrome. The findings suggest that digital tools hold substantial promise in reducing burnout levels, paving the way for improved early detection, prevention, and management strategies for dentistry burnout syndrome. The study also demonstrates the transformative potential of integrating entropy analysis and artificial intelligence in healthcare to provide more refined and predictive models for managing work-induced stress and burnout.

1. Introduction

Burnout syndrome is an increasingly recognized occupational hazard in the field of dentistry, manifesting as emotional exhaustion, depersonalization, and reduced personal accomplishment [1]. These symptoms contribute to diminished professional fulfillment and heightened cynicism, particularly in high-pressure clinical environments [2]. Dental professionals are especially vulnerable to burnout due to the constant physical, emotional, and mental demands of their work, which involve maintaining intricate patient relationships, making critical decisions, and managing high workloads [3]. The study of dentistry burnout syndrome (DBS) has undergone significant advancements over the past few decades. Early work by Maslach et al. [1] introduced the foundational psychological dimensions of burnout encapsulated in the Maslach burnout inventory. This tool has been widely used to assess emotional exhaustion, depersonalization, and reduced professional accomplishment, making it a cornerstone in burnout research [4,5,6,7,8]. However, traditional tools like the Maslach burnout inventory face limitations, particularly in their ability to account for the multifactorial and dynamic nature of burnout in high-stress professions such as dentistry, where both psychological and practical challenges are at play [9]. To address these limitations, Schaufeli and Bakker [10] introduced the job demands–resources model, which offers a more comprehensive framework by linking job demands (e.g., workload, emotional strain) and job resources (e.g., support, autonomy) to outcomes such as burnout and work engagement. While the job demands–resources model broadens the understanding of burnout by acknowledging the interaction between demands and resources, it still falls short in its ability to dynamically model the intricate and evolving nature of burnout in real-time environments [11].

Data on DBS prevalence in Portugal are scarce. However, a 2012 study found that 35% of family doctors in Portugal experienced emotional exhaustion, 32% reported depersonalization, and 12% had high scores across all burnout dimensions [12]. Meta-analyses from 2022 and 2023 estimated a global burnout prevalence of 13% among dentists, with higher rates in Europe [13,14]. Early detection and intervention strategies are critical. However, existing diagnostic tools for DBS often fall short of capturing the multifaceted nature of burnout, which involves both subjective experiences and objective workload-related stressors [15]. In recent years, technological advancements, particularly in the realm of digital tools such as intraoral scanners, cone beam computed tomography (CBCT), digital photography, and treatment planning software, have transformed the clinical landscape of dentistry [16]. These tools not only enhance diagnostic accuracy and streamline treatment workflows, but also have the potential to alleviate some of the stressors that contribute to DBS [16]. However, the precise impact of these digital tools on burnout levels among dental professionals remains underexplored. In recent years, the integration of artificial intelligence (AI) and entropy-based methods has emerged as a promising approach to advancing burnout diagnosis and prediction [17]. Obermeyer and Emanuel [18] highlighted the significant role of machine learning in medical diagnostics, emphasizing its capability to detect subtle patterns in large datasets that traditional tools might overlook. This potential for precision and scalability makes AI an ideal candidate for enhancing DBS assessments in dentistry [19]. Simultaneously, the use of entropy in burnout studies has gained traction, as researchers like Thurner et al. [17] have demonstrated the effectiveness of entropy in quantifying disorder and unpredictability in complex systems. By applying entropy to burnout data, it is possible to measure the randomness or variability within stress-related metrics, offering a novel lens through which the disorder inherent in burnout can be understood. In fact, the application of AI and entropy-based methods offers a new avenue for assessing this impact. AI models, particularly those employing machine learning, can analyze large datasets to simulate and predict burnout levels based on various factors, including the use of digital tools [17]. Entropy, which measures disorder and complexity [17,20,21,22], provides a framework for understanding the unpredictability and multifactorial nature of burnout [23]. Together, AI and entropy allow for a more nuanced evaluation of how digital tool adoption in dental practices influences burnout, accounting for both direct and indirect effects on workflow, patient interaction, and decision making [18,23].

Despite advancements in digital tools, their specific impact on burnout levels among dental professionals remains underexplored. These tools are increasingly integrated into dental practices to streamline workflows and reduce manual tasks [16]. However, it is essential to determine whether they alleviate burnout or contribute to stress through technological overload. Thus, this study aims to evaluate the impact of digital tools on burnout levels among dental professionals. The motivation stems from the growing adoption of these technologies in daily practice and their potential to reduce time pressures, improve patient communication, and optimize workflows. The key contribution of this work lies in its innovative methodology, which combines entropy analysis and AI to offer a comprehensive framework for assessing DBS. By utilizing entropy to measure the disorder and complexity within the burnout data and AI-driven logic programming (LP) models to predict burnout intensities, this approach provides a more nuanced and accurate understanding of the factors contributing to burnout. This dual analysis not only enhances the precision of DBS diagnosis, but also identifies the specific impact of digital tools on mitigating or exacerbating burnout symptoms. Furthermore, the study’s ability to quantify burnout through entropic measures, coupled with AI’s predictive capabilities, opens new avenues for personalized interventions and early detection strategies tailored to the individual needs of dental professionals. This study is fundamentally rooted in engineering principles, applying AI-driven solutions and entropy analysis to predict and assess burnout among dental professionals. By integrating engineering methodologies, such as predictive modeling, system optimization, and data analysis, it offers a structured, quantitative approach to understanding burnout dynamics. The study’s interdisciplinary nature highlights how engineering techniques can be effectively applied in healthcare settings.

2. Using Entropy for Knowledge Representation

In this piece, a groundbreaking methodology is introduced to assess entropic efficiency as a vehicle for problem solving. It postulates that entropy spans between 0 and 1, with a lower value depicting order and a higher one symbolizing disorder or chaos [20,21,22]. This avant-garde methodology is grounded in the concept of knowledge representation and reasoning (KRR), a specialized sector of AI. KRR strives to portray knowledge in a form discernible by machines and to formulate algorithms that can exploit this knowledge to execute intelligent functions [24]. Structured languages, such as first-order logic, description logic, and frame-based systems, are intrinsic to KRR for the systematic organization of knowledge. Subsequently, this structured, machine-readable knowledge is leveraged by reasoning algorithms to resolve queries, make informed decisions, and address problems.

This strategy draws parallels between KRR and the degradation of energy observed in the laws of thermodynamics. It conveys that, over a period, the energy accessible for work attenuates. This methodology’s foundational principles are substantiated by the first and second laws of thermodynamics. The first law, denoting the law of energy conservation, articulates that the cumulative energy in an isolated system remains invariant, implying energy transformation rather than creation or destruction. The second law unfolds the notion of entropy, detailing a system’s organized state and its evolution, thereby making this method especially suitable for KRR, where the energy depicting the entropic state of a system is perceived as degradable but indestructible energy [21,25,26,27]. The energy quotient that remains usable after a transfer operation, or, in essence, the entropic state of the discourse universe, is often delineated as follows:

• Exergy, indicating available energy;
• Vagueness, illustrating the energy quantities that might or might not be relocated and consumed;
• Anergy, depicting the untouched energy potential.

This approach, integrating thermodynamics and AI through the lens of entropy and knowledge representation, provides a structured and logical inference framework to solve intricate problems.

Integrating computational collective intelligence with areas such as knowledge representation, thermodynamic laws, and mathematical problem solving deepens the framework for computational data interpretation and algorithm development [28,29]. The flexibility and effectiveness of this combined methodology make it highly applicable to various situations, offering value in a broad spectrum of case studies [30,31]. The core of the approach is its interdisciplinary nature, using thermodynamic metaphors to examine AI’s capabilities and boundaries [32]. Its novelty and adaptability allow it to be applied to a wide range of cases [33]. As an example, combining KRR with thermodynamic principles provides a dynamic framework for evaluating AI systems, emphasizing energy efficiency and entropy as critical factors in driving performance and sustainability [33]. Many other relevant case studies focus on complex data landscapes [34], especially in big data analytics and cloud computing, where managing computational resources efficiently is paramount [35]. In the field of decision-making systems, particularly in autonomous vehicles and financial systems, where precision and reliability are critical, these strict logical frameworks guarantee that AI’s decisions are both reliable and verifiable [35]. The adaptability of this framework over time makes it highly effective in areas such as robotics and adaptive learning platforms, where AI needs to react autonomously to changing environments [36]. Furthermore, the interdisciplinary nature of the framework allows it to be applied in areas such as healthcare and environmental science, making it essential for developing AI solutions that address ethical, sustainability, and technical concerns [37].

Entropy and knowledge are deeply connected concepts in information theory, thermodynamics, and epistemology [38,39]. In information theory, entropy measures uncertainty or randomness in a system. Higher entropy means more unpredictability. Knowledge reduces entropy by organizing and structuring information, which decreases uncertainty. Before flipping a coin, entropy is maximal because the outcome is unknown. Once the result is observed, entropy drops to zero because uncertainty is eliminated [40]. In thermodynamics, entropy represents disorder in a physical system. Acquiring knowledge can be seen as a process of reducing disorder in an epistemic sense. From an epistemological perspective, knowledge structures and organizes data, reducing randomness. According to Jaynes [41], the principle of maximum entropy can be used to infer knowledge from incomplete information. Entropy also limits knowledge, as seen in quantum mechanics and chaos theory, where uncertainty plays a key role in defining what can and cannot be known [42]. Gaining knowledge reduces informational entropy, while uncertainty increases it. A comprehensive discussion of these concepts can be found in [38,39].

3. Materials and Methods

This section provides a summary of the research setup, including the framework, data acquisition methods, tools used for measurement, sample characteristics, and analysis techniques. It also briefly mentions the ethical protocols adhered to in the study.

3.1. Study Design

The study aims to assess burnout levels among dental professionals and evaluate the impact of digital tools, such as intraoral scanners, CBCT, digital photography, and treatment planning software, on burnout syndrome in dentistry. It also seeks to determine the effectiveness of entropy- and AI-based methods in predicting and mitigating burnout. By examining the integration of these tools into daily practice, the study explores whether they alleviate or exacerbate burnout. The research questions are as follows:

What are the contributing factors to DBS among dental professionals?

How do digital tools in dentistry influence burnout levels, and can entropy- and AI-based methods improve the prediction and management of DBS?

To address these questions, a questionnaire was designed to cover key areas relevant to DBS, including job satisfaction, AI-powered tools, time and communication efficiency, and patient expectations and litigation. The responses to these categories were analyzed using an entropy-based methodology, transforming qualitative feedback into quantitative data [37]. By evaluating the patterns of burnout with the use of digital tools, the study seeks to provide data-driven insights into areas where burnout prevention strategies can be enhanced.

3.2. Data Collection

The decision to adopt a survey-based approach using questionnaires followed a comprehensive assessment of various data collection methods. Given the simplicity, flexibility, and capacity to standardize responses, questionnaires were deemed most appropriate for this study. Additionally, the use of anonymous surveys encouraged candid responses on sensitive issues, such as burnout. While in-depth methods like interviews and focus groups offer richer context, they are resource-intensive and less efficient for large-scale studies. Moreover, questionnaires provide a straightforward means to convert qualitative data into quantitative data for statistical analysis, particularly when applying AI and entropy-based methods [43,44,45,46].

The questionnaire used in this study encompasses three subdivisions designed to collect comprehensive data on the factors contributing to burnout and the impact of AI-powered digital tools on DBS. The first subdivision covers sociodemographic information, collecting details such as gender, age group, years of experience, and type of dental practice. These variables are key to understanding burnout levels in relation to professional and personal backgrounds. The second subdivision features several statements (

Table 1. Sections and statements featured in the second subdivision of the questionnaire.

Job Satisfaction (JS—3)	S1	I have enough time to devote to my patients.
	S2	I am happy with my current workload.
	S3	My patients value my work.
AI-Powered Tools (AIPT—4)	S4	Photographic camera is indispensable in dental clinics.
	S5	Intraoral scanner is indispensable in dental clinics.
	S6	Cone beam equipment is crucial in dental clinics.
	S7	I am satisfied with the equipment I have.
Time and Communication (TC—4)	S8	I can communicate the treatment plan with clarity to my patients.
	S9	I can communicate the treatment plan with clarity to staff.
	S10	I can communicate the treatment plan with clarity to the dental laboratory.
	S11	I can show the patient the result before starting the treatment.
Patient Expectations (PE—4)	S12	I am often able to meet my patient’s expectations.
	S13	I always have a pleasant relationship with my patients.
	S14	My patients do not complain about long, uncomfortable treatments.
	S15	My patients often like the aesthetic result.

) that explore the key areas of the study, including job satisfaction—3 sentences (JS—3), artificial intelligence-powered tools—4 sentences (AIPT—4), time and communication—4 sentences (TC—4), and patient expectations—4 sentences (PE—4). The JS—3 section evaluates burnout-related factors like workload intensity, emotional resilience, and professional fulfillment. In this section, participants evaluated how these factors influenced their well-being and stress levels in the workplace, particularly regarding patient interaction and clinical demands. The focus of the AIPT—4 section was on the role of digital tools like intraoral scanners, CBCT, digital photography, and treatment planning software. Participants evaluated whether these tools helped reduce burnout by improving efficiency, communication, and clinical outcomes. In the TC–4 section, participants evaluated their time management efficiency and communication effectiveness within the dental team, with patients, and with external partners like dental labs. They also assessed how digital tools improved these aspects and potentially reduced stress. The PE—4 section aims to assess how patient satisfaction and the quality of dentist–patient interactions affect the dentist’s emotional well-being and contribute to burnout levels. Finally, in the third subdivision, participants were asked to share their opinions on the impact of digital tools on DBS (i.e., a relevant, neutral, or irrelevant impact on DBS levels).

The second subdivision of the questionnaire used a five-level Likert scale (strongly agree, agree, disagree, strongly disagree, and I don’t know) to capture participants’ agreement with each statement. Participants were encouraged to reflect on their professional experiences and indicate how the use of digital tools impacted their burnout levels. They were also asked to specify whether their responses followed a positive trend (moving from strongly disagree to strongly agree) or a negative one (from strongly agree to strongly disagree). In the final subdivision, a three-level Likert scale (positive, neutral, and negative) was used.

To ensure the questionnaire’s validity and relevance, a validation process following Bell’s protocol [47] was conducted. A panel of five experts from dental practice management, AI in healthcare, occupational burnout, and social science research reviewed the initial questionnaire and suggested refinements, which were incorporated into the reviewed version. The validity and clarity of the revised questionnaire were tested with a small pilot group of participants, who were not included in the main study. Their feedback helped refine the questionnaire, which was subsequently distributed at the 1st Artificial Intelligence in Dentistry Congress—Intelligent Dentistry 2024, held in Faro, Portugal from April 26 to 27 and attended exclusively by dental professionals. The refined version questionnaire’s reliability was evaluated using Cronbach’s alpha, yielding a coefficient of 0.88 for the questions in the second subdivision of the questionnaire. The data collection was carried out during and immediately after the conference, ensuring a 100% return rate, with all 132 distributed questionnaires returned.

3.3. Participants

Of the 132 forms distributed, 6 (4.5%) were discarded due to missing sociodemographic information. Hence, the study includes an opportunity sample comprising 126 dental practitioners, aged from 25 to 65, with a mean age of 39.2 ± 9.5, with both genders represented.

Table 2. Demographic distribution of participants by gender, age group, years of experience, and type of dental practice.

Demographic Variable	Category	Frequency
		N	%
Gender	Female	71	56.3
	Male	55	43.7
Age Group (years old)	25–35	38	30.2
	36–45	50	39.7
	46–55	26	20.6
	56–65	12	9.5
Years of Experience	1–5 years	28	22.2
	6–10 years	42	33.3
	11–15 years	32	25.4
	more than 16 years	24	19.1
Type of Dental Practice	General dentistry	64	50.8
	Specialized practice	35	27.8
	Academic/Research	12	9.5
	Other 1	15	11.9

¹ e.g., freelance dentists working in multiple dental offices.

outlines the participants’ demographics, including gender, age group, years of experience in the dental profession, and type of dental practice.

3.4. Qualitative Data Processing

In the second subdivision of the questionnaire, a five-level Likert scale (strongly disagree (1), disagree (2), neutral (3), agree (4), strongly agree (5)) was used to record participants’ agreement with the presented statements. To allow for a more thorough analysis of participants’ responses, considering the progression of their opinions, the scale was expanded to nine levels:

strongly agree (5), agree (4); neutral (3), disagree (2), strongly disagree (1), disagree (2), neutral (3), agree (4), strongly agree (5)

This mirrored nine-level Likert scale allows for two interpretations as follows:

• From left to middle, i.e., from strongly agree (4) to strongly disagree (1), reflecting a trend toward more negative opinions;
• From middle to right, i.e., from strongly disagree (1) to strongly agree (4), reflecting a trend toward more positive opinions.

The qualitative responses were transformed into quantitative data using the geometric approach presented in [48]. The responses regarding each key area were mapped onto a circle of radius ${\pi }^{-1/2}$, partitioned into sections matching the number of statements in that area, with each response option corresponding to a mark on the axis. This framework allows for comparisons between KRR and thermodynamic principles, establishing parallels with energy degradation processes [25,27]. Thus, a data unit is considered to be in an entropic state, allowing its energy to be divided into exergy, vagueness, and anergy, enabling degradation without destruction. This approach is detailed and exemplified in Section 4.2.

3.5. Artificial Neural Networks

The artificial neural network (ANN) models were constructed using WEKA 3.8.6 software with default configurations [49,50], employing the backpropagation algorithm and logistic activation function for training [51,52,53]. To guarantee reliable results, each experiment was repeated 20 times. The dataset was partitioned into 67% for model training and 33% for testing, where the training data were used to build the model and the test data were used to assess its ability to generalize.

3.6. Ethical Aspects

All participants were fully informed of the study’s objectives and voluntarily took part without coercion. The Ethics Committee of CESPU University approved the study (CE/IUCS/CESPU-13/22) on 21 April 2022, ensuring adherence to the highest ethical standards. Informed consent was obtained from all participants, and personal data were anonymized.

4. Results and Discussion

This section presents the findings of a study evaluating the impact of digital tools on burnout levels among a cohort of 126 dental professionals who were recruited during a dental conference.

4.1. Frequency of Responses Analysis

In this section, a frequency of responses analysis will be conducted to examine how participants distributed their answers across the different response categories. To enhance data interpretation, bar charts will be used to illustrate response patterns. Additionally, the trends identified will be discussed and compared with existing studies.

Figure 1 points out the frequencies of the responses related to job satisfaction, i.e., statements S1 to S3. The data indicate that more than 90% of participants responded positively (i.e., agree or strongly agree) to S1 (on time devoted to patients) and S2 (on current workload). Conversely, 84.1% of participants responded negatively (i.e., disagree or strongly disagree) to S3 (on patients’ evaluation of their work), suggesting that some dentists feel underappreciated by their patients. Moreover, the percentage of participants with a neutral opinion on this group of statements ranged from 1.6% for S2 to 8.7% for S3. Indeed, 8.7% of participants had a neutral opinion on their patients’ evaluation of their work. Consequently, only 7.2% of participants reported having a positive opinion of the evaluation their patients make of their work.

Figure 2 shows the frequency of responses related to AI-powered tools (statements S4 to S7). Analysis of the data present in this figure indicates that more than two-thirds of participants (i.e., 66.7%) responded positively (i.e., agree or strongly agree) to S4 (on the indispensability of photographic cameras), S6 (on the indispensability of cone beam equipment), and S7 (on satisfaction with the equipment available). In contrast, 50.8% of participants expressed a negative response (i.e., disagree or strongly disagree) to S5 (on the indispensability of intraoral scanners), indicating that most of the surveyed dentists do not consider this type of equipment essential for their practice. The percentage of participants with a neutral opinion on this group of statements ranged from 15.1% for S7 to 22.2% for S6. A possible justification for the relatively high percentage of dentists with a neutral opinion on the indispensability of photographic cameras, intraoral scanners, and cone beam equipment in dental practice could be attributed to several factors, including a lack of familiarity or experience with these technologies. Dentists who have not received sufficient training or exposure to advanced dental technologies during their education and professional development may be less inclined to perceive them as essential [54]. Additionally, the high costs associated with acquiring and maintaining such equipment can be a significant barrier, particularly for smaller or solo practices [55]. Some dentists might also believe that traditional equipment suffices for their needs, thus not seeing the added value of investing in newer technologies [3]. Furthermore, the perceived necessity of these technologies can vary across different dental specialties, with certain practices finding them less critical than others. For instance, general dentists may benefit more from intraoral scanners than specialists whose treatments do not rely heavily on digital imaging. These factors can collectively contribute to a neutral stance on the indispensability of advanced dental technologies among a subset of dentists.

Figure 3 highlights the frequency of responses regarding time and communication efficiency (statements S8 to S11). The analysis of the data depicted in this figure indicates that about two-thirds of participants (i.e., 66.7%) responded positively (i.e., agree or strongly agree) to S8 (on the communication of the treatment plan with patients), S9 (on the communication of the treatment plan with staff), and S11 (on showing the results before starting the treatment). Contrariwise, 50.8% of participants expressed a negative response (i.e., disagree or strongly disagree) to S10 (on the communication with the dental laboratories). The integration of technology into dental practice plays a crucial role in enhancing communication between dentists and lab technicians. Despite the potential benefits, there may be communication barriers or misunderstandings between dentists and dental laboratories, and/or dentists might have high expectations regarding communication and collaboration with dental labs, and the reality may not meet these expectations [54,55]. The percentage of participants with a neutral opinion on the statements related to time and communication efficiency ranged from 19.8% for S9 and S10 to 22.2% for S8 and S11. This relatively high percentage suggests that improving communication between dentists, patients, staff, and dental labs, as well as enhancing methods to present results before starting treatment, could be key areas for improvement. Therefore, implementing advanced communication technologies could bridge this gap, fostering better collaboration and efficiency within dental teams [55,56].

Figure 4 exhibits the frequency of responses regarding patient expectations (statements S12 to S15). The analysis reveals that the majority of participants responded positively (i.e., agree or strongly agree) to all statements, with frequencies ranging from 43.7% for S14 (on the absence of complaints about long and uncomfortable treatments) to 60.3% for S15 (on the aesthetic outcomes of the treatments). Furthermore, the frequency of negative responses (i.e., disagree or strongly disagree) is below 10% for all statements, except for S14, where this percentage was 14.3%. Moreover, the percentage of participants with a neutral opinion on the statements related to patient expectations ranged from 14.3% for S15 to 30.2% for S14. A possible justification for the relatively high percentage of dentists with a neutral opinion on this topic is that dentists from different specialties or practice settings may have varied experiences. For example, general dentists encounter different patient types and treatments compared to specialists, which can influence their ability to evaluate these statements consistently. As patient interactions can vary significantly, dentists may find it difficult to form a definitive opinion, leading some to remain neutral in their responses [54,55,56].

In the third subdivision of the questionnaire, participants were asked to share their opinions on the impact of digital tools on DBS. Most participants (53.2%) selected positive impact, noting that digital tools streamline routine tasks, reduce administrative burdens, and improve workflow efficiency, thereby contributing to a reduction in burnout [54]. Conversely, 20.6% of participants selected negative impact, noting that digital tools contribute to an increase in burnout. In their view, digital tools can increase workload in other ways, such as learning new systems, troubleshooting technical issues, and the constant need to stay updated with software—factors that can add stress [3,56]. Finally, 26.2% of participants ticked neutral impact, considering that digital tools are neither significantly helpful nor harmful in terms of burnout. While they may offer some efficiency gains, they might not have a strong emotional impact on work-related stress or overall well-being [57,58].

4.2. Entropy-Based Methodology for Data Handling

Figure 5 displays participant one’s responses to the second subdivision of the questionnaire, along with the trend of their response evolution. To convert qualitative data to a quantitative format and ensure comprehensibility, full details for JS—3 (statements S1 through S3) are outlined. Thus, in the case of statement S1, participant one selected agree and demonstrated a positive trend (Figure 5). Consequently, this response should be assessed along the scale ranging from strongly disagree (1) to strongly agree (5), considering strongly agree as the best-case scenario (BCS) and agree as the worst-case scenario (WCS), as shown in

Table 3. Conversion of participant one’s responses to statements S1 through S15, segmented by the key areas of the study (job satisfaction, AI-powered tools, time and communication, and patient expectations) to a mirrored nine-level Likert scale.

Key Area	Statements	Mirrored Nine-Level Likert Scale *
		Negative Trend					Positive Trend
		5	4	3	2	1		2	3	4	5
Job Satisfaction (JS—3)	S1									×	×
	S2		×	×
	S3	×
AI-Powered Tools (AIPT—4)	S4	×
	S5			×	×
	S6	×
	S7							×	×
Time and Communication (TC—4)	S8	×
	S9									×	×
	S10									×	×
	S11		×	×
Patient Expectations (PE—4)	S12	×
	S13									×	×
	S14		×	×
	S15	×

* (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree.

. Regarding statement 2, the participant also selected the option agree, but showed a negative trend in the evolution of their opinion. In this instance, the response should be assigned to the negative scale, from strongly agree (5) to strongly disagree (1), with agree as the BCS and neutral as the WCS (Table 3). Finally, for statement S3, participant one selected strongly agree without indicating any trend. However, this response should be placed on the negative scale, from strongly agree (5) to strongly disagree (1), as it can either remain the same or worsen in the future (Table 3).

In Figure 6, participant one’s responses to the second subdivision of the questionnaire are visually depicted, showing both BCS and WCS. Orange regions denote exergy, indicating usable energy, mustard yellow areas represent vagueness, suggesting uncertainty or partially utilized energy, and white areas illustrate anergy, which represents the potential energy that remains unused [37,59].

The responses for each key area were mapped onto a circle with a radius of ${\pi }^{-1/2}$, divided into sections based on the number of statements in that area. Each response option was assigned a specific mark on the axis, with radii of ${\pi }^{-1/2}/5, 2{\pi }^{-1/2}/5, 3{\pi }^{-1/2}/5, 4{\pi }^{-1/2}/5,$ and $5{\pi }^{-1/2}/5$, corresponding to strongly agree, agree, neutral, disagree, and strongly disagree, respectively. The areas corresponding to exergy (orange), vagueness (mustard yellow), and anergy (white) in Figure 6 were calculated using Equations (1)–(5):

$$A_{Exergy}^{BCS and WCS}={\displaystyle \frac{1}{Q}}\pi r^2$$

(1)

$$A_{Vagueness}^{BCS}=0$$

(2)

$$A_{Vagueness}^{WCS}={\displaystyle \frac{1}{Q}}\pi R^2-{\displaystyle \frac{1}{Q}}\pi r^2={\displaystyle \frac{1}{Q}}\pi \left(R^2-r^2\right)$$

(3)

$$A_{Anergy}^{BCS}={\displaystyle \frac{1}{Q}}\pi {\left({\pi }^{-1/2}\right)}^2-{\displaystyle \frac{1}{Q}}\pi r^2={\displaystyle \frac{1}{Q}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-r^2\right]$$

(4)

$$A_{Anergy}^{WCS}={\displaystyle \frac{1}{Q}}\pi {\left({\pi }^{-1/2}\right)}^2-{\displaystyle \frac{1}{Q}}\pi R^2={\displaystyle \frac{1}{Q}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-R^2\right]$$

(5)

where $Q$ represents the number of statements per key area ($Q=3$ for job satisfaction, and $Q=4$ for the remaining areas), $r$ is the radius corresponding to the most positive response, and $R$ is the radius corresponding to the least positive response. Thus, for job satisfaction, one may have the following possibilities:

• In statement S1, the options agree and strongly agree were considered (Table 3), so $r={\displaystyle \frac{1}{5}}{\pi }^{-1/2},$ and $R={\displaystyle \frac{2}{5}}{\pi }^{-1/2}$;
• In statement S2, the options agree and neutral were considered (Table 3), so $r={\displaystyle \frac{2}{5}}{\pi }^{-1/2},$ and $R={\displaystyle \frac{3}{5}}{\pi }^{-1/2}$;
• In statement S3, only the option strongly agree was considered (Table 3), so $r=R={\displaystyle \frac{1}{5}}{\pi }^{-1/2}$

The evaluation of the regions depicted in Figure 6 covering both scales (from strongly agree (5) to strongly disagree (1) and vice versa) in relation to job satisfaction is presented in

Table 4. Evaluating exergy, vagueness, and anergy for participant one’s responses to statements S1 through S3 regarding job satisfaction, in the best-case scenario, for both scales, i.e., from strongly agree (5) to strongly disagree (1), and from strongly disagree (1) to strongly agree (5).

Statements		Scale (5) → (1)	Scale (1) → (5)
Job Satisfaction (JS—3)	S1	$-$	$exerg{y}_{S_1}={\displaystyle \frac{1}{3}}\pi {\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2=0.013$
		$-$	$vaguenes{s}_{S_1}=0$
		$-$	$anerg{y}_{S_1}={\displaystyle \frac{1}{3}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2\right]=0.320$
	S2	$exerg{y}_{S_2}={\displaystyle \frac{1}{3}}\pi {\left({\displaystyle \frac{2}{5}}{\pi }^{-1/2}\right)}^2=0.053$	$-$
		$vaguenes{s}_{S_2}=0$	$-$
		$anerg{y}_{S_2}={\displaystyle \frac{1}{3}}\pi {\left({\pi }^{-1/2}\right)}^2-$ $anerg{y}_{S_2}={\displaystyle \frac{1}{3}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{2}{5}}{\pi }^{-1/2}\right)}^2\right]=0.280$	$-$
	S3	$exerg{y}_{S_3}={\displaystyle \frac{1}{3}}\pi {\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2=0.013$	$-$
		$vaguenes{s}_{S_2}=0$	$-$
		$anerg{y}_{S_1}={\displaystyle \frac{1}{3}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2\right]=0.320$	$-$

for BCS and

Table 5. Evaluating exergy, vagueness, and anergy for participant one’s responses to statements S1 through S3 regarding job satisfaction, in the worst-case scenario, for both scales, i.e., from strongly agree (5) to strongly disagree (1), and from strongly disagree (1) to strongly agree (5).

Statement		Scale (5) → (1)	Scale (1) → (5)
Job Satisfaction (JS—3)	S1	$-$	$exerg{y}_{S_1}={\displaystyle \frac{1}{3}}\pi {\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2=0.013$
		$-$	$vaguenes{s}_{S_1}={\displaystyle \frac{1}{3}}\pi \left[{\left({\displaystyle \frac{2}{5}}{\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2\right]=0.040$
		$-$	$anerg{y}_{S_1}={\displaystyle \frac{1}{3}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{2}{5}}{\pi }^{-1/2}\right)}^2\right]=0.280$
	S2	$exerg{y}_{S_2}={\displaystyle \frac{1}{3}}\pi {\left({\displaystyle \frac{2}{5}}{\pi }^{-1/2}\right)}^2=0.053$	$-$
		$vaguenes{s}_{S_2}={\displaystyle \frac{1}{3}}\pi \left[{\left({\displaystyle \frac{3}{5}}{\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{2}{5}}{\pi }^{-1/2}\right)}^2\right]=0.067$	$-$
		$anerg{y}_{S_2}={\displaystyle \frac{1}{3}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{3}{5}}{\pi }^{-1/2}\right)}^2\right]=0.213$	$-$
	S3	$exerg{y}_{S_3}={\displaystyle \frac{1}{3}}\pi {\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2=0.013$	$-$
		$vaguenes{s}_{S_3}=0$	$-$
		$anerg{y}_{S_1}={\displaystyle \frac{1}{3}}\pi \left[{\left({\pi }^{-1/2}\right)}^2-\right.$         $\left.-{\left({\displaystyle \frac{1}{5}}{\pi }^{-1/2}\right)}^2\right]=0.320$	$-$

for WCS.

The values of exergy, vagueness, and anergy for participant one, in relation to job satisfaction, within BCS, on the scale from strongly agree (5) to strongly disagree (1), are determined using the values listed in Table 4, as follows:

$$exerg{y}_{{JS-3}_{5-1}}=exerg{y}_{5-1_{S_2}}+exerg{y}_{5-1_{S_3}}=0.053+0.013=0.066$$

$$vaguenes{s}_{{JS-3}_{5-1}}=vaguenes{s}_{5-1_{S_2}}+vaguenes{s}_{5-1_{S_3}}=0$$

$$anerg{y}_{{JS-3}_{5-1}}=anerg{y}_{5-1_{S_2}}+anerg{y}_{5-1_{S_3}}=0.280+0.320=0.600$$

Similarly, for the scale ranging from strongly disagree (1) to strongly agree (5), the values for participant one in the BCS are:

$$exerg{y}_{{JS-3}_{1-5}}=exerg{y}_{1-5_{S_1}}=0.013$$

$$vaguenes{s}_{{JS-3}_{1-5}}=vaguenes{s}_{1-5_{S_1}}=0.$$

$$anerg{y}_{{JS-3}_{1-5}}=anerg{y}_{1-5_{S_1}}=0.320$$

Using the framework presented above, the exergy, vagueness, and anergy values for all participants can be computed. These values, for all key areas covered in the study for participant one, are shown in

Table 6. Values of exergy (EX), vagueness (VA), and anergy (AN) regarding participant one for all key areas covered in the study (job satisfaction (JS—3), artificial intelligence-powered tools (AIPT—4), time and communication (TC—4), and patient expectations (PE—4)) in the best-case scenario, for both scales, i.e., from strongly agree (5) to strongly disagree (1), and from strongly disagree (1) to strongly agree (5).

	Scale (5) → (1)				Scale (1) → (5)
	EX	VA	AN		EX	VA	AN
JS—35-1	0.066	0	0.600	JS—31-5	0.013	0	0.320
AIPT—45-1	0.110	0	0.640	AIPT—41-5	0.090	0	0.160
TC—45-1	0.050	0	0.450	TC—41-5	0.020	0	0.480
PE—45-1	0.060	0	0.690	PE—41-5	0.010	0	0.240

and

Table 7. Values of exergy (EX), vagueness (VA), and anergy (AN) regarding participant one for all key areas covered in the study (job satisfaction (JS—3), artificial intelligence-powered tools (AIPT—4), time and communication (TC—4), and patient expectations (PE—4)) in the worst-case scenario, for both scales, i.e., from strongly agree (5) to strongly disagree (1), and from strongly disagree (1) to strongly agree (5).

	Scale (5) → (1)				Scale (1) → (5)
	EX	VA	AN		EX	VA	AN
JS—35-1	0.066	0.067	0.533	JS—31-5	0.013	0.040	0.280
AIPT—45-1	0.110	0.070	0.570	AIPT—41-5	0.090	0.070	0.090
TC—45-1	0.050	0.050	0.400	TC—41-5	0.020	0.060	0.420
PE—45-1	0.060	0.050	0.640	PE—41-5	0.010	0.030	0.210

for the BCS and WCS, respectively.

To predict the impact of digital tools on DBS, ANNs were used. A database including the exergy, vagueness, and anergy values for each of the four key areas, corresponding to each of the 126 participants involved in the study, was created in both BCS and WCS. These values (calculated based on the information collected in the second subdivision of the questionnaire) served as input variables. The data from the third section of the questionnaire were used as the output variable (Figure 7).

To evaluate the effectiveness of the ANN model (Figure 8), the confusion matrix shown in

Table 8. Confusion matrix of the ANN model for predicting the impact of digital tools on dentistry burnout syndrome.

	Predict	Training			Test
Target		Relevant Impact	Neutral Impact	Irrelevant Impact	Relevant Impact	Neutral Impact	Irrelevant Impact
Relevant Impact		42	3	0	21	1	0
Neutral Impact		2	19	1	2	8	1
Irrelevant Impact		0	2	17	0	1	6

was used. Using the information in Table 8, the model’s accuracy (calculated as the proportion of correct predictions out of the total) was 90.7% for the training set (78 out of 86 cases correctly identified) and 87.5% for the test set (35 out of 40 cases correctly identified). This demonstrates that the ANN model is effective in predicting the impact of digital tools on DBS, with accuracy levels above 85%.

To evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ANN model, a confusion matrix was created for each output, as shown in

Table 9. Confusion matrix concerning each output class of the ANN model for predicting the impact of digital tools on dentistry burnout syndrome.

	Predict	Training Set		Test Set
Target		Relevant Impact	Non-Relevant Impact	Relevant Impact	Non-Relevant Impact
Relevant Impact		42	3	21	1
Non-Relevant Impact		2	39	2	16
		Neutral Impact	Non-Neutral Impact	Neutral Impact	Non-Neutral Impact
Neutral Impact		19	3	8	3
Non-Neutral Impact		5	59	2	27
		Irrelevant Impact	Non-Irrelevant Impact	Irrelevant Impact	Non-Irrelevant Impact
Irrelevant Impact		17	2	6	1
Non-Irrelevant Impact		1	66	1	32

. Sensitivity represents the proportion of positive cases (relevant, neutral, or irrelevant) correctly identified as positive, while specificity reflects the proportion of negative cases (non-relevant, non-neutral, or non-irrelevant) correctly identified as negative. PPV indicates the ratio of accurately classified relevant, neutral, or irrelevant cases, whereas NPV refers to the ratio of correctly identified non-relevant, non-neutral, or non-irrelevant cases [60,61].

Table 10. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each output class of the ANN model for predicting the impact of digital tools on dentistry burnout syndrome.

Class	Training Set				Test Set
	Sensitivity	Specificity	PPV	NPV	Sensitivity	Specificity	PPV	NPV
Relevant Impact	0.93	0.95	0.96	0.93	0.96	0.89	0.91	0.94
Neutral Impact	0.86	0.92	0.79	0.95	0.73	0.93	0.80	0.90
Irrelevant Impact	0.90	0.99	0.94	0.97	0.86	0.97	0.86	0.97

presents the computed values for the metrics previously discussed. The model shows good performance in assessing the impact of digital tools on DBS, as reflected by high sensitivity and specificity values ranging from 0.73 to 0.99. This is further supported by high PPV and NPV values, which range from 0.79 to 0.97. This model offers a valuable understanding of dental professionals’ well-being, highlights areas where burnout risks may arise, and supports decision making by prioritizing actions based on projected outcomes. By forecasting different potential situations, the model provides numerous benefits for managing burnout in dental professionals, such as enabling early intervention by identifying various risk factors and challenges and for improving overall well-being by recognizing stressors and addressing them more efficiently. Built on past data, including feedback from dental professionals and relevant performance indicators, the model forecasts how changes in factors such as workload, job satisfaction, and support systems influence burnout levels. It identifies critical factors with the greatest impact, highlights areas of concern, and assists in prioritizing interventions based on their potential outcomes. Furthermore, the model predicts possible obstacles and aids in the effective distribution of resources, ensuring a proactive approach to managing burnout. Through regular updates and adjustments, the ANN model encourages continuous enhancement by responding to the shifting demands and concerns of dental professionals. In future developments of this study, other techniques will be tested, like random forest and genetic algorithms, just to name a few [62,63].

5. Conclusions

This study examines dental professionals’ perceptions of burnout in relation to factors such as job satisfaction, AI-powered tools, time management and communication, and patient expectations. The results indicate that while most professionals view AI-powered tools positively, areas such as patient perceptions of treatment, the necessity of acquiring intraoral scanners, and communication with dental laboratories require further improvement. Overall, dental professionals perceive digital tools as beneficial in mitigating burnout, although improvements are needed, particularly in the areas mentioned above. The study highlights the potential of AI-powered tools to reduce stress and improve efficiency in dental practices, while also identifying key areas, such as communication with patients and satisfaction with equipment, that could further alleviate burnout. Additionally, a predictive model based on artificial neural networks was developed to forecast the impact of digital tools on dentistry burnout syndrome. The model demonstrated good effectiveness, achieving accuracies above 85% and enabling proactive management by predicting various potential stressors and scenarios within the dental work environment. The methodology used in this work combines thermodynamic principles with AI-based tools, offering a structured framework to understand the complex causes of burnout. It allows for the quantification of these factors and supports the creation of predictive models for early intervention. This adaptable structure can be fine-tuned to address the requirements of various organizations. To optimize the model’s relevance, modifications to the data collection methods and the study focus may be required, depending on the organization’s characteristics. Future research could benefit from a larger sample size to examine how demographic factors may influence participants’ views on the role of digital tools in dentistry burnout syndrome, as well as to obtain more generalizable results.