Dentistry in the Era of Artificial Intelligence: Medical Behavior and Clinical Responsibility

Abstract

Objectives: Digitalization has revolutionized dentistry, introducing advanced technological tools that improve diagnostic accuracy and access to healthcare. This study aims to examine the effects of integrating digital technologies into the dental field, analyzing the associated benefits and risks, with particular paid attention to the therapeutic relationship and decision-making autonomy. Materials and Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and Cochrane Library, complemented by Google Scholar for non-indexed studies. The selection criteria included peer-reviewed studies published in English between 2014 and 2024, focusing on digital dentistry, artificial intelligence, and medical ethics. This is a narrative review. Elements of PRISMA guidelines were applied to enhance transparency in reporting. Results: The analysis highlighted that although digital technologies and AI offer significant benefits, such as more accurate diagnoses and personalized treatments, there are associated risks, including the loss of empathy in the dentist–patient relationship, the risk of overdiagnosis, and the possibility of bias in the data. Conclusions: The balance between technological innovation and the centrality of the dentist is crucial. A human and ethical approach to digital medicine is essential to ensure that technologies improve patient care without compromising the therapeutic relationship. To preserve the quality of dental care, it is necessary to integrate digital technologies in a way that supports, rather than replaces, the therapeutic relationship.

1. Introduction

The medical act, in its traditional essence, has a diagnostic–therapeutic purpose, based on the doctor–patient relationship and the integration of signs, symptoms, and clinical data to formulate a diagnosis and define treatment [1]. Digitalization in dentistry is not a recent phenomenon but has developed in parallel with the evolution of information and biomedical technologies. The introduction of digital radiodiagnostics, CAD/CAM techniques, and guided surgery has revolutionized clinical practice, reducing surgical variability and increasing the reliability of treatments [2].

These advances have improved diagnostic precision, reduced the invasiveness of interventions, and promoted more equitable access to care through telemedicine and the digitalization of clinical data [3]. However, critical issues remain, including the risk of compromising the doctor–patient relationship, delegating decision-making to automated systems or unauthorized personnel, and the algorithmic standardization of medicine, with the danger of reverting to forms of medical paternalism [4].

The integration of digital technologies and artificial intelligence (AI) has transformed multiple areas of dentistry, affecting diagnostics, treatment planning, and patient management. AI-driven systems, such as deep learning models for radiographic analysis, virtual planning software, and AI-assisted diagnostic tools, have shown substantial benefits. The widespread use of CAD/CAM systems, intraoral scanners, and digital workflows has further contributed to reducing clinical variability and enhancing treatment predictability. Despite these advances, AI’s role in dentistry must be critically examined to balance its advantages with the associated risks. Increasing reliance on AI raises concerns related to clinician autonomy, overdiagnosis, underdiagnosis, and ethical issues, such as algorithmic bias and lack of transparency.

The integration of digital tools into the diagnostic and therapeutic process requires rigorous regulatory oversight and adequate professional training. Medicine cannot be reduced to a technical process: the human element, empathy, and personalized care remain irreplaceable. Moreover, the excessive medicalization of society, with constant monitoring of clinical parameters and indiscriminate use of digital technologies, calls for a bioethical reflection to strike a balance between innovation and protection of the therapeutic relationship [4,5]. If properly governed, technological progress can improve the efficiency and accessibility of care, but it must be accompanied by the responsible use that mitigates its risks. Artificial intelligence in medicine cannot overlook the central role of the doctor, whose ability to understand suffering, modulate information, and assume therapeutic responsibility remains essential.

The integration of AI in dentistry holds great promise for enhancing diagnostic accuracy, such as the early detection of childhood caries and plaque. However, despite growing awareness of AI’s potential, several barriers hinder its adoption. A recent survey of 375 pediatric dentists indicated that while 62% were familiar with AI, significant obstacles persist, including high costs (83%) and lack of postgraduate training (78%). This highlights the need for targeted education and training programs to facilitate AI implementation in pediatric dental practice [6].

The purpose of this narrative review is to provide an interdisciplinary perspective on the integration of AI in dentistry, emphasizing the need for regulatory clarity and ethical responsibility. This study aims to evaluate the clinical significance of AI, its current applications, and the challenges associated with its implementation, ultimately guiding future research and policy development. The future challenge will be to integrate digital tools without compromising the doctor–patient relationship, reaffirming that technology must serve to improve clinical practice without undermining its ethical and relational principles [4,5,7].

2. Materials and Methods

This narrative review of the literature was conducted to explore and synthesize the main evidence related to digital dentistry, artificial intelligence, medical ethics, humanization of care, and teledentistry. The methodology followed the PRISMA guidelines for narrative reviews to ensure transparency and reproducibility (see Supplementary Materials). The bibliographic search was performed using four main scientific databases, PubMed, Scopus, Web of Science, and the Cochrane Library, with the additional integration of Google Scholar to include non-indexed studies.

The search strategy used a combination of MeSH terms and Boolean operators, including

• “Artificial Intelligence” AND “Dentistry”;
• “Machine Learning” OR “Deep Learning” AND “Oral Health”;
• “Decision Support Systems” AND “Dental Ethics”;
• “Neural Networks” AND “Teledentistry”.

Inclusion criteria:

• Articles published in English between 2014 and 2024;
• Relevant to the topics of digital dentistry, artificial intelligence, medical ethics, and humanization of care;
• Peer-reviewed articles, including original studies, meta-analyses, narrative reviews, and clinical cases;
• Research addressing ethical, clinical, and regulatory aspects of AI.

Exclusion criteria:

• Studies not related to dentistry;
• Non-peer-reviewed literature (e.g., blogs, corporate reports);
• Studies without validated quantitative or qualitative data.

Bias assessment was performed using the Newcastle–Ottawa Scale (NOS) for observational studies and the AMSTAR 2 tool for systematic reviews, ensuring that included studies met rigorous methodological standards.

The search yielded 275 articles. After screening for relevance and methodological rigor, 82 studies were included for thematic analysis. Themes were identified through an inductive approach, which classified findings into key categories:

(1)

AI-driven diagnostics

(2)

Clinical decision support

(3)

Ethical concerns

(4)

Regulatory challenges

(5)

Patient–dentist relationship dynamics

The retrieved results were examined based on thematic relevance and methodological quality, with particular attention to studies exploring the ethical and relational aspects of integrating digital technologies into dentistry. Consensus on the key themes was achieved through discussion among the authors, who independently analyzed and categorized the articles, ensuring a comprehensive representation of the topic.

This study is a narrative review and does not represent a systematic or scoping review. Nevertheless, we partially applied elements of the PRISMA 2020 checklist to enhance transparency in reporting. A PRISMA flow diagram summarizing the search and selection process is provided Figure 1.

The 2014–2024 timeframe was selected to capture the pivotal decade of AI integration in dentistry. This period aligns with the proliferation of deep learning for diagnostic imaging [8], the widespread adoption of digital workflows (e.g., intraoral scanning, CAD/CAM), and the development of critical ethical frameworks addressing AI-specific risks in healthcare [5,9]. Earlier studies lack clinical-scale validation of modern AI tools.

Limitations of the study

Although this review provides an in-depth exploration of the integration of artificial intelligence in dentistry, some limitations should be acknowledged.

Firstly, as a narrative review, the selection of articles and thematic synthesis were based in part on the authors’ interpretative criteria. While elements of the PRISMA framework were applied, the process lacks the systematic rigor of a registered review protocol.

Secondly, publication bias cannot be excluded. The review only includes peer-reviewed studies published in English, excluding gray literature and non-English research that might offer different perspectives.

Thirdly, the interdisciplinary nature of the study—spanning clinical, ethical, and legal aspects—may have led to varied levels of depth across themes. While this broad approach strengthens the general overview, it may limit the specificity of technical or procedural insights.

Lastly, patient-centered studies and qualitative analyses reflecting patient perspectives were underrepresented, suggesting a need for future research focused more explicitly on the user experience and real-world implementation of AI in dental care.

3. Results

3.1. The Relationship Between Dentist and Patient in the Digital Era

Digital evolution has profoundly transformed dental practice, improving diagnostic accuracy, therapeutic efficiency, and access to care. However, it has also redefined the dentist–patient relationship, posing new challenges in terms of trust, empathy, and communication. The increasing use of artificial intelligence (AI), advanced imaging, and decision-making software has changed the role of the clinician, raising questions about their centrality in the diagnostic and therapeutic process [8].

Digital tools have improved doctor–patient communication through digitized reports, 3D simulations, and teleconsultations. However, the risk of dehumanization of the medical act is concrete: if not properly managed, digitalization can reduce human contact and compromise the quality of the therapeutic relationship. Communication in dentistry is not limited to the transmission of clinical information but includes empathic interaction, which is essential for treatment adherence and the psychological well-being of the patient [10].

A critical aspect of digitalization is the patient’s perception. On the one hand, three-dimensional images and simulations improve the understanding of procedures; on the other, technological mediation can make the interaction more impersonal. There is a risk that the doctor is perceived merely as an interpreter of machine-generated data, or that the patient mistakes anyone managing the data for medical personnel [11].

The human element remains essential: empathy, active listening, and the modulation of language based on the patient’s needs cannot be replaced by technology. Trust in the doctor–patient relationship depends on clear and personalized communication, which cannot be reduced to interactions mediated exclusively by digital tools. The dentist must therefore maintain a central role, balancing innovation with the humanization of care [5].

Excessive reliance on technologies risks making the dentist–patient relationship more detached and mechanistic. Digitalization must not distance the patient from the professional, but should be integrated into diagnostic and therapeutic processes without compromising the relational dimension [4,5,6,7].

Digital dentistry must find a balance between technological innovation and the centrality of the dentist–patient relationship. Technology should support the clinician without replacing their irreplaceable role in decision-making and therapy. The future of the profession depends on the ability to integrate innovation while preserving empathy, trust, and personalized care [12].

3.2. Risks of Unregulated Use of AI in Dentistry

Artificial intelligence (AI) is revolutionizing dentistry by improving diagnostic accuracy, operational efficiency, and treatment planning. However, the lack of a clear regulatory framework raises ethical, medico-legal, and clinical concerns; the unregulated use of AI can compromise the quality of care and patient safety [9].

A key limitation lies in diagnostic and therapeutic standardization: algorithms analyze recurring patterns but may fail to account for patient-specific complexity. “Algorithmic medicine” risks reducing personalization, especially in early diagnostic stages [13].

Several regions have begun to introduce regulatory frameworks:

• United States: The FDA has issued guidelines emphasizing clinical validation, ongoing monitoring, and transparency regarding AI capabilities [14].
• European Union: The proposed Artificial Intelligence Act classifies AI systems by risk level and imposes strict requirements for high-risk medical applications, including human oversight and accountability [15].
• Japan: The AI Network Society initiative promotes responsible AI integration, though specific healthcare legislation is still evolving [16,17].

Further risks include algorithmic bias, often stemming from unbalanced training datasets, and the “black box” problem, which impairs clinical validation of AI outputs [18].

Safe integration of AI into dental care requires:

• Clinical validation through controlled trials;
• Medical supervision to support, not replace, judgment;
• Algorithmic transparency and traceability;
• Ongoing professional training regarding medico-legal implications.

AI presents an extraordinary opportunity, but it cannot substitute for the dentist. Innovation must be balanced with professional responsibility and patient-centered care [19].

3.3. The Pervasive Use of Technologies and the Risks of Overdiagnosis and Overtreatment

Digital semiotics has enriched the diagnostic process without replacing it. Tools such as intraoral scanners, 3D imaging, and AI enhance diagnostic accuracy and speed [9]. However, their widespread use presents challenges, including the risk of overdiagnosis and overtreatment.

If not adequately regulated, these technologies may lead to unnecessary procedures, identifying minor alterations that are clinically irrelevant yet still prompt intervention. This increases the risk of iatrogenesis and psychological distress for patients [8,9,17].

Excessive monitoring may also create anxiety and unrealistic expectations, potentially harming the dentist–patient relationship and increasing medico-legal conflicts [20].

A balanced approach that integrates data interpretation skills, ethical responsibility, and transparent communication is essential to ensure the appropriateness and quality of care [1,21].

3.4. Risks Related to Overdiagnosis and Psychological Impact on Patients

Digital technologies can amplify the detection of anomalies with limited or no clinical relevance, increasing the risk of unnecessary treatment. When clinicians are influenced by unfiltered AI-generated data, the likelihood of overtreatment rises, along with the risk of iatrogenic harm and anxiety in patients [13].

Instant access to detailed imagery may distort patient expectations, leading to dissatisfaction and disputes if results fall short of idealized outcomes [22,23].

Clear, realistic communication is vital to manage expectations and prevent misunderstandings that could lead to medico-legal consequences.

3.5. Summary of Key Findings

To enhance clarity, the main findings of the review are summarized in

Table 1. A summary of key findings from the literature review on the impact of artificial intelligence and digital technologies in dentistry, highlighting their benefits, challenges, and ethical implications.

Theme	Key Findings
AI-driven diagnostics	Increased accuracy in detecting caries, periodontal disease, and oral cancer
Decision Support	AI improves treatment planning but requires human oversight
Ethical concerns	Algorithmic bias and lack of explainability raise ethical and legal issues
Regulatory challenges	Lack of harmonization in AI governance across different jurisdictions
Patient–dentist relationship	AI enhances efficiency but risks depersonalizing patient interactions

.

Additionally, the review identified 82 articles, categorized as follows:

• Randomized controlled trials (18 studies);
• Observational studies (24 studies);
• Systematic reviews (16 studies);
• Meta-analyses (12 studies);
• Narrative reviews (12 studies).

4. Discussion

The discussion elaborates on the implications of AI-driven advancements in dentistry, contrasting the study’s findings with the existing literature. Unlike previous reviews that primarily focus on the technical capabilities of AI, this work provides an ethical and regulatory perspective. AI’s ability to enhance diagnostic precision is well documented; however, concerns about underdiagnosis, overdiagnosis, and patient safety remain pressing issues.

A key observation is the regulatory fragmentation across different regions, which complicates the implementation of AI in global dental practice. The need for standardized governance, clear medico-legal frameworks, and increased clinician training emerges as crucial for responsible AI integration.

4.1. Bioethics and Personalization of Care

Bioethics is a fundamental component of medical practice, guiding decisions toward both scientific soundness and respect for patient dignity and equity. The core principles, autonomy, beneficence, non-maleficence, and justice, must be preserved in the adoption of new technologies, ensuring that innovation does not compromise the human value of care [5].

The use of chatbots and AI systems in clinical contexts raises ethical challenges. These tools lack awareness and interpretative ability, which may oversimplify complex decision-making processes [7].

The principle of autonomy, which involves respect for the patient’s will and informed consent, is challenged by the probabilistic nature of AI systems. These tools cannot fully adapt to individual preferences or contextual nuances, which may lead to misunderstandings or a reduction in truly informed decision-making [5,24].

Beneficence, or acting in the patient’s best interest, may also be limited. AI algorithms are based on statistical data and lack the capacity to consider the psychosocial aspects of a patient’s condition. Clinical reasoning, which integrates biological, emotional, and social variables, cannot be replicated by a machine [24,25].

Regarding non-maleficence, the presence of bias in training datasets remains a significant threat. If data are not representative of diverse populations, the risk of incorrect or inequitable recommendations increases. The “black box” nature of some AI systems also makes it difficult to evaluate the reliability of outputs [7,24,25].

Lastly, justice can be compromised due to unequal access to technology or underrepresentation in datasets, which can lead to disparities in outcomes [7,26].

In addition to these principles, ethical virtues such as prudence, empathy, and professional responsibility must be upheld. Prudence involves a cautious, critical use of AI. Empathy remains irreplaceable in the doctor–patient relationship. Responsibility must stay firmly with the clinician, ensuring that AI remains a support and not a substitute for medical judgment [27].

4.2. The Role of the Dentist in Technological Innovation

Technological progress has greatly expanded the tools available in dental practice, including AI-based radiographic analysis, CAD/CAM design, and guided surgical systems. However, the dentist’s central role remains essential to interpret digital data, adapt it to the clinical context, and ensure ethical care [28].

The dentist must not become a passive executor of algorithmic protocols. Instead, they should act as a critical interpreter, combining scientific knowledge with personal judgment and clinical experience [5,8].

A frequent risk is the standardization of care at the expense of individual variability. Digital integration must serve the personalization of treatment, not its reduction to automated outputs. Continuous professional development is essential to ensure that the clinician is able to evaluate, integrate, and, where necessary, question the results provided by AI [29].

Key competencies include

• Understanding the logic of AI systems to use them critically;
• Managing digital data while ensuring privacy and regulatory compliance;
• Adopting a multidisciplinary perspective, collaborating with professionals in bioinformatics, ethics, and engineering [30].

Digital dentistry offers extraordinary opportunities, but the dentist must remain the core of the diagnostic and therapeutic process. Technology should enhance—but never replace—clinical reasoning and the relational dimension of care [31,32,33].

4.3. Digital Paternalism and Informed Consent

Digital paternalism refers to the uncritical delegation of clinical decisions to AI systems, either by dentists or patients. The perception of AI as infallible can diminish patient autonomy, especially when therapeutic choices are guided more by algorithmic suggestions than by personalized dialogue [34,35].

This can also affect clinicians, who may rely excessively on digital recommendations, leading to ambiguity in liability when adverse outcomes occur. To address this, algorithmic decisions must be transparent, limitations must be communicated clearly, and clinician training must be reinforced [36].

Patient digital literacy also plays a key role in supporting shared decision-making. Without adequate understanding, there is a risk of overconfidence in automated responses, weakening the informed consent process [10].

AI integration into evidence-based medicine (EBM) must maintain traceability and verifiability. The “black box” effect prevents clinicians from confirming whether recommendations are based on robust evidence or biased patterns [37,38].

To mitigate this, AI systems should be designed with explainability, auditability, and clinical accountability in mind [39,40].

4.4. Decision-Making Autonomy and the Role of the Doctor in the Era of AI

Artificial intelligence in medicine must be understood as a decision support tool, not a substitute for human clinical judgment. Medical decisions require the integration of not only biological data, but also emotional, psychological, and social factors. While AI algorithms in radiology and digital pathology can offer reliable preliminary evaluations, their autonomy must remain limited and regulated [40].

The integration of AI into clinical pathways calls for an update of medical training and professional development. Healthcare professionals must be prepared to engage critically with algorithmic outputs, recognizing when AI-based suggestions require re-evaluation or human contextualization. Without this, there is a risk of de-responsibilization, where clinicians might feel less accountable for decisions suggested or reinforced by AI [41,42].

Moreover, the widespread adoption of digital technologies in health monitoring introduces additional ethical concerns. The continuous tracking of patient parameters may not only contribute to overdiagnosis and overtreatment but may also infringe on privacy—especially if used by insurance companies or corporate entities seeking to limit access to care or increase premiums [10].

To ensure ethical integration, strict regulatory oversight is needed for both data management and digital devices, striking a balance between innovation and patient rights [5,42]. AI in healthcare must enhance diagnostic and therapeutic capabilities without ever replacing human clinical authority.

The digital medical act must preserve human control as its central component. The future of healthcare requires tools that respect human dignity, ensure accountability, and uphold the values of ethical medicine [43].

4.5. AI Transparency Strategies to Strengthen Decision-Making

To reinforce the clinician’s decision-making autonomy in the age of AI, transparency is essential. One of the most promising approaches is the development of explainable AI (XAI) systems, which offer understandable, traceable reasoning behind each output. Instead of delivering “black box” conclusions, these systems provide visualizations, logic trees, or stepwise justifications that allow healthcare professionals to grasp how a decision is reached.

This transparency enables the critical evaluation of AI outputs by clinicians, helping them validate or contest the system’s conclusions. Additionally, explainable systems can support clearer patient communication, reinforcing trust and informed consent by offering interpretable justifications for diagnoses or treatment options.

Other essential components of transparent AI integration include the following areas:

• Documentation of training datasets and the populations represented;
• Explicit validation protocols, including statistical thresholds and clinical performance metrics;
• Real-time feedback mechanisms, such as confidence scores, predictive risk ranges, and reliability indices.

Finally, continuous system updating based on new data and clinical guidelines is crucial to ensure that AI remains not only transparent, but also relevant, evidence-based, and adaptable to evolving clinical scenarios.

These strategies help to preserve human oversight, support clinical reasoning, and enhance patient safety and trust.

4.6. Risks Related to Underdiagnosis

Underdiagnosis represents a significant concern in the clinical application of AI tools, particularly when dealing with complex or rare conditions in dentistry. While AI systems are trained to detect patterns with high sensitivity, they are only as effective as the data they are trained on.

If training datasets are limited or unbalanced, AI tools may fail to recognize atypical presentations of disease or rare conditions not well represented during model development. For example, subtle early symptoms or outlier cases may be overlooked or misclassified, leading to missed diagnoses and inappropriate management strategies.

Moreover, AI algorithms may prioritize certain clinical features over others, undervaluing weaker or non-standard signs that an experienced clinician would consider relevant. As a result, clinicians could unknowingly rely on incomplete or skewed outputs, thereby reducing diagnostic sensitivity in real-world cases.

To mitigate this risk, the following must be true:

• AI systems must be trained on diverse, representative datasets.
• Their performance must be continually validated in multicentric, heterogeneous clinical environments.
• Clinicians must receive adequate training to understand when AI may be insufficient or misleading and when additional investigation or a second opinion is warranted.

In all cases, human interpretation and clinical judgment remain indispensable to correcting for the blind spots and inherent limitations of AI systems.

4.7. Future Perspectives and Recommendations

Artificial intelligence is increasingly shaping the future of dentistry by improving diagnostic accuracy, therapeutic planning, and operational efficiency [4]. As technology advances, future developments may include adaptive learning algorithms that evolve based on clinical experience and integrate a patient’s biological, genetic, and historical data to support precision medicine [3].

However, to ensure a responsible transition, AI must remain a decision support tool, not a substitute for the dentist’s expertise. Technology must serve clinicians—not the other way around [36,39].

One particularly promising area is the use of neural networks (NNs) for early-stage oral cancer detection. A recent systematic review found that NNs reached diagnostic accuracies above 85%, underscoring their potential. However, all included studies presented a high risk of bias, and some reported applicability concerns, indicating that further high-quality research is essential to validate AI’s clinical utility [44].

While this review focused on AI-driven diagnostics and patient management, future applications could revolutionize therapeutic areas such as personalized bone regeneration and tissue engineering. Emerging studies suggest that AI may optimize scaffold design, predict graft integration, and customize growth factor delivery [45].

The interdisciplinary collaboration between dentists, bioinformaticians, engineers, ethicists, and legal experts will be key to ensuring that AI systems are well-calibrated, safe, and ethically grounded. Combining 3D imaging, clinical records, genetic profiles, and treatment history into unified platforms could allow AI to support more personalized and predictive therapeutic strategies [30,46].

Teledentistry, expected to expand in the coming years, could enhance access to care, particularly in remote or underserved areas. However, its implementation must be accompanied by strong data protection measures and clear regulatory frameworks to avoid the risk of professional de-responsibilization. Remote consultations should be used as complementary tools, not replacements for physical visits [36,47].

4.8. Recommendations for Ethical and Effective Use of Digital Technologies

To ensure the ethical and effective use of AI and digital technologies, a balanced approach is needed that integrates innovation and tradition [8]. A clear regulatory framework must govern the use of AI, ensuring the transparency and traceability of algorithmic decisions [10]. Professionals must be trained to critically interpret digital tools, avoiding excessive dependence on technology [43].

It is essential to maintain a personalized approach to care, as each patient is unique, and the treatment plan must reflect this individuality [15]. Furthermore, it is essential to ensure transparency in the use of AI, reducing the risk of errors resulting from biases in datasets and countering the “black box” problem [18].

Technology must improve the patient experience, without replacing human contact. The adoption of digital tools must be accompanied by strategies that preserve empathy and mutual trust as fundamental elements of the therapeutic relationship [43]. Clear communication and the conscious management of new technologies are crucial to balance innovation and clinical appropriateness, improving the quality of dental care and reducing the risk of unnecessary interventions [33,36].

4.9. The Patient’s Perspective in AI-Driven Dentistry

While this review has focused on ethical, clinical, and regulatory aspects, future research should also incorporate the patient’s perspective. Understanding how individuals perceive AI-based diagnostics, their concerns about data privacy, or their comfort with automated systems is essential for promoting trust and adherence.

Including qualitative studies and patient-reported outcome measures (PROMs) will help ensure that digital innovations are aligned not only with clinical standards but also with user expectations and values.

5. Conclusions

This review aimed to explore the evolving role of artificial intelligence (AI) in dentistry, with specific attention paid to its ethical implications and the necessity of preserving the human element in digital healthcare.

While the integration of digital technologies into clinical practice is not a new topic, this article contributes to the current discourse by examining the intersection of technological innovation and the therapeutic relationship. Unlike previous studies focused primarily on technical performance, this review emphasizes the importance of a structured and ethically conscious approach to AI integration.

AI tools can undoubtedly improve diagnostic accuracy and therapeutic planning. However, overreliance on technology introduces risks such as algorithmic bias, underdiagnosis, overdiagnosis, and reduced clinician autonomy. This is particularly critical in dentistry, where attention to the individual patient’s needs is a central component of care.

To be effective and ethically sound, AI integration must support clinical decision-making without undermining empathy, transparency, and relational trust. The human role in diagnosis and treatment must not be diminished in the name of efficiency or automation.

Furthermore, the study highlights the value of interdisciplinary collaboration among clinicians, ethicists, legal experts, and technologists. Such collaboration is essential to developing AI systems that are clinically valid, ethically responsible, and aligned with the values of patient-centered care.

In conclusion, this review advocates for a balanced and ethically guided integration of AI in dentistry. Only by addressing the challenges and limitations of these technologies can we ensure that AI enhances clinical practice while safeguarding the integrity of the dentist–patient relationship in an increasingly digital world.