The Absent Professional in Digital Dentistry Patient Perception, Therapeutic Alliance and Trust

Gilberto Salas Abad^1*

¹Medical Stomatologist, Digital Orthodontist, Private Practice in Alcoy, Spain

*Correspondence author: Gilberto Salas Abad, Medical Stomatologist, Digital Orthodontist, Private Practice in Alcoy, Spain;
E-mail: [email protected]

Citation: Abad GS. The Absent Professional in Digital Dentistry Patient Perception, Therapeutic Alliance and Trust. J Dental Health Oral Res. 2025;6(3):1-11.

Copyright^© 2025 by Abad GS. All rights reserved. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Table 1: Items of the initial OrtoBotAI questionnaire (Likert scale 1-5).

Response instructions: Indicate your level of agreement with each statement on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree).

Table 1 presents the items of the affective mapping questionnaire specifically developed to explore clinical predispositions relevant to adherence in digital orthodontic treatments. This brief instrument consists of ten Likert-type items (1–5) covering dimensions such as need for validation, hypervigilance, affective avoidance, low frustration tolerance and guilt associated with noncompliance. The questionnaire was not intended to diagnose pathological structures but to generate a dynamic relational profile, anticipating how each patient interprets the orthodontist’s absence. These profiles provided the basis for subsequent personalization of the symbolic signs delivered through the OrtoBotAI platform.

Table 2: Item-dimension correspondence and scoring guidelines.

Table 2 summarizes the preliminary assignment of questionnaire items to hypothesized clinical dimensions. This operational scheme constitutes the first step toward validation of the instrument. By calculating mean scores per dimension and applying orientative cutoffs, it was possible to identify the relative intensity of each trait. For instance, high scores on items 2, 4 and 5 indicated an increased need for validation, while items 6 and 10 captured avoidance tendencies. Although this represents version 0.1 of the classification system and remains subject to psychometric validation, the framework already provided a basis for designing communicative strategies adapted to patients’ affective predispositions.

Table 3: Observable clinical configurations and adaptive symbolic signs.

Table 3 synthesizes the critical step of the methodological design: translating affective profiles into observable clinical configurations with corresponding communicative strategies. Each configuration was described in terms of its predominant traits, main needs and adaptive symbolic responses. For instance, patients with high validation needs received frequent reminders and positive reinforcement, while avoidant patients were approached with minimal but precise technical communication. Hypervigilant patients benefited from transparency and consistency, whereas idealizing/rejecting patients required realistic expectations and stable messaging. Patients with guilt tendencies responded positively to normalization of deviations and supportive feedback. This operationalization allowed OrtoBotAI to transform abstract affective tendencies into concrete digital protocols aimed at sustaining adherence and the therapeutic alliance.

Results

The preliminary validation of OrtoBotAI included more than 100 adult patients (18-55 years) undergoing aligner treatment with digital follow-up. Data were collected through structured interviews, self-report questionnaires and digital behavior logs on the platform, generating both quantitative and qualitative evidence on adherence, satisfaction and transferential conflicts.

Global Outcomes

Aggregated indicators (Fig. 1) demonstrated consistent improvements.

Figure 1: Global outcomes of adherence, satisfaction and transferential conflicts. Bar graph showing overall improvements with OrtoBotAI compared with baseline digital monitoring.

Overall adherence increased by 25% compared with baseline digital monitoring without symbolic adaptation, in line with evidence on adherence challenges in removable orthodontic appliances [18]. Transferential conflicts-such as complaints, perceived abandonment or resistance to platform use-were reduced by 40% in hypervigilant and idealizing patients. Subjective satisfaction reached 85%, which exceeds the average values typically reported for teledentistry and digital monitoring (70-75%) [3,18,19]. These findings suggest that adaptive symbolic mediation can stabilize both behavioral and experiential variables in digital orthodontics.

Outcomes by Clinical Configuration

Disaggregated results (Fig. 2) highlighted distinct patterns across the five clinical profiles:

• Excessive validation: adherence 90%, satisfaction 88% (highest values). Frequent reminders and empathetic reinforcement proved decisive
• Guilt profile: adherence 85%, satisfaction 83%. Normalization of minor deviations and positive feedback enhanced resilience to treatment lapses
• Idealization/rejection: adherence 70%, satisfaction 68% (lowest values). Initial enthusiasm tended to decline when expectations were unmet, underscoring the need for improved symbolic strategies
• Avoidant profiles: adherence 75%, satisfaction 72%. A low-affective, technical communication style was effective but less engaging
• Hypervigilant profiles: adherence 80%, satisfaction 78%. Transparency and consistent reporting reduced suspicion and increased trust

Figure 2: Outcomes by clinical configuration. Comparison of adherence and satisfaction rates across five patient profiles (validation, hypervigilance, avoidance, idealization/rejection, guilt).

Statistical Analysis

Descriptive and inferential analyses confirmed significant improvements. Mean adherence across the sample was 82% (SD ± 8.5), compared to 65% (SD ± 10.2) at baseline (p < 0.01). Mean satisfaction reached 81% (SD ± 7.9), significantly higher than baseline values (69% ± 9.1, p < 0.01). Analysis of variance (ANOVA) revealed significant differences among profiles (F = 5.43; p < 0.01). Post-hoc Tukey tests indicated that the idealization/rejection profile scored significantly lower than all other groups (p < 0.05), while the validation profile scored significantly higher (p < 0.05). Reduction in transferential conflicts was also statistically significant (χ² = 12.6; p < 0.01).

Table 4: Statistical results by clinical profile.

Table 4 shows that the validation profile achieved the highest outcomes, with adherence of 90% ± 5.2 and satisfaction of 88% ± 6.0, both significantly higher than baseline (p < 0.01). The guilt profile also demonstrated strong results (85% ± 6.0 adherence; 83% ± 5.9 satisfaction; p < 0.05). In contrast, the idealization/rejection group obtained the lowest values (70% ± 8.1 adherence; 68% ± 7.9 satisfaction), which were not statistically different from baseline (n.s.). Avoidant and hypervigilant profiles produced intermediate results (75–80% adherence; 72–78% satisfaction), both significantly improved compared with baseline (p < 0.05). These findings confirm the heterogeneity of symbolic responses across clinical profiles and reinforce the importance of adaptive personalization in sustaining adherence and satisfaction.

Qualitative Observations

Patient interviews revealed that graphic progress indicators (superimpositions, growth charts, timelines) were consistently valued as tangible evidence of treatment efficacy. Anxious and hypervigilant patients emphasized relief when the system provided consistent information, while avoidant patients preferred minimal but precise interactions. The idealization/rejection group frequently described oscillating experiences, alternating between enthusiasm and disappointment, consistent with attachment-related interpretations of digital absence [14,15].

Overall, the adaptive symbolic framework of OrtoBotAI improved adherence, reduced transferential conflicts and enhanced patient satisfaction. These findings support the hypothesis that symbolic presence, when carefully designed, can effectively compensate for the orthodontist’s physical absence in digital contexts, offering continuity, trust and a therapeutic alliance comparable to in-person care [3,14,15,18,19].

Discussion

The findings suggest that the orthodontist’s physical absence can be compensated through adaptive symbolic signs, provided these are designed in alignment with the patient’s subjective structure. This supports the psychoanalytic hypothesis that lack does not eliminate desire but organizes it, as long as absence is consistently symbolized [11,12]. In this sense, absence should not be understood as emptiness but rather as a productive space of meaning, where the transferential bond finds new anchors in digital signs. Within the Lacanian framework, the promise of orthodontic treatment is inscribed in a field of desire mediated by words, signs and images. Symbolic presence, like physical presence, can sustain transference when it establishes continuity and coherence.

From a Deleuzian perspective, the system functions as a productive machine of meaning: the patient’s experience is oriented toward verifiable flows (graphs, timelines, overlays) that anchor transformation in tangible records [13]. This conceptualization of desire as production shifts the notion of lack toward that of assemblage, emphasizing that digital clinical practice is not a deficient substitute for in-person care but a field where new modalities of bonding and accompaniment are created. Patients engage with digital signs-messages, visualizations, metrics-as a horizon of validation that allows them to perceive treatment as a dynamic, ongoing process, thereby sustaining adherence even in the orthodontist’s absence.

From a clinical perspective, personalization is essential. Excessive digital presence for patients with avoidant traits may be perceived as intrusive, generating resistance and disengagement. Conversely, insufficient contact for patients with anxious attachment can be experienced as abandonment, compromising therapeutic continuity [14,15]. OrtoBotAI addresses this challenge by calibrating the frequency, tone and content of communication according to each detected profile, thereby reducing dissonance between subjective expectations and digital input. In doing so, teledentistry moves beyond the simple transmission of information toward a symbolic design of clinical accompaniment, where absence is reframed not as a deficit but as a modulated clinical variable.

Compared with telemedicine literature where perceived availability often outweighs actual physical presence-this study adds specificity to orthodontics by introducing symbolic coherence as a variable of transferential regulation [4]. While immediacy of response is often prioritized in other medical contexts, the present findings highlight transparency (for hypervigilant patients) and consistency (for idealizing patients) as key factors for reducing persecutory projections and preventing relational ruptures. In other words, the challenge lies not merely in being available but in generating communicative signs that convey reliability and symbolic stability.

From a methodological standpoint, several limitations must be acknowledged. The sample size, although sufficient for exploratory purposes, requires replication in larger and more diverse populations. Follow-up was restricted to the initial months of treatment, precluding assessment of long-term effects. Furthermore, the absence of a randomized control group limited the robustness of comparisons with non-personalized digital care. Finally, measurement of adherence and satisfaction, though based on established indicators, could be strengthened by validated instruments and triangulation with objective records of aligner wear, such as data from smart monitoring devices [18]. The ethical implications of these findings warrant special attention. Artificial intelligence should not be conceived as a substitute for the orthodontist but rather as a redistribution of symbolic function. The system mediates presence but does not replace clinical supervision. In this regard, it is essential to ensure informed consent, clearly specifying the scope of automation and delineating when human intervention is required. Escalation protocols must also be established for critical scenarios, such as emotional crises, sustained drops in adherence or technical events beyond the system’s capacity. The ethics of digital clinical practice likewise require transparency in data management, protection of patient privacy and traceability of all symbolic interactions [21,22].

Another concern is the risk of homogenization in the design of digital signs. Although personalization is a guiding principle, technological trends often favor standardization. The challenge lies in preserving subjective singularity within automated environments, avoiding the impression that patients are passive recipients of impersonal algorithms. Psychoanalytic and attachment theories provide a framework that underscores structural variability in patient experiences and supports the design of digital clinical practice as a flexible and adaptive space [11,14,15].

In summary, the results indicate that the orthodontist’s physical absence does not necessarily weaken the therapeutic bond, provided that symbolic devices are coherently aligned with the patient’s subjective structure. Rather than an obstacle, absence can become a clinical resource that, when symbolized appropriately, structures desire and sustains adherence. OrtoBotAI exemplifies how this hypothesis can be operationalized in practice, offering a model of symbolically informed teledentistry that not only transmits information but also fosters accompaniment, continuity and trust [4,18]. The modular design of OrtoBotAI supports integration of multiple diagnostic and therapeutic components into a single symbolic framework (Fig. 3).

Figure 3: OrtoBotAI modular ecosystem. Diagram of the integrated architecture of OrtoBotAgent and associated modules (Ceph, DIA, Track, Mov, Print, Manager). Modules are distinguished by status (active vs. in development), with bidirectional data flow through the central hub.

Diagram showing the integrated architecture of OrtoBotAgent and its associated modules (Ceph, DIA, Track, Mov, Print and Manager). Modules are classified by status (active vs. in development), with bidirectional data flow managed through the central hub.

Significance Statement

This study introduces the concept of symbolic presence as a clinical resource in digital orthodontics, demonstrating that adaptive communication strategies can effectively compensate for the orthodontist’s physical absence. By integrating psychoanalytic and attachment frameworks with artificial intelligence, the OrtoBotAI platform operationalizes subjective dimensions of care through personalized symbolic signs. Preliminary results show significant improvements in adherence, satisfaction and reduction of transferential conflicts, highlighting that digital orthodontics must be designed not only for technical precision but also for affective resonance. These findings open a new paradigm in digital healthcare where AI redistributes, rather than replaces, the symbolic function of the clinician.

Conclusion

The preliminary validation of OrtoBotAI shows that adaptive symbolic signs can significantly improve adherence, satisfaction and reduce transferential conflicts in digital aligner treatments. These findings suggest that symbolic strategies represent a clinically relevant complement to technical monitoring, particularly for patients with anxious or hypervigilant profiles. Although limited by sample size and short follow-up, the results highlight the need for larger controlled studies to evaluate long-term effects and generalizability across diverse populations. Ethical considerations remain central: informed consent, patient privacy and escalation protocols must accompany the design of symbolic mediation systems. Future developments should integrate OrtoBotAI modules into broader digital orthodontics ecosystems, testing how symbolic presence interacts with biomechanical precision to sustain therapeutic alliance in hybrid human-AI models.

Conflict of Interest

There are no conflicts of interest that may have influenced the research, authorship or publication of the article.

Financial Disclosure

This study was self-funded. No external financial support was received.

Acknowledgments

The author thanks the patients who participated in the preliminary validation of OrtoBotAI and acknowledges the contribution of colleagues who provided feedback on the symbolic design of the platform.

Ethical Statement

This project was exempt from IRB review as it did not qualify as human subject research under federal regulations.

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