Unleashing the Future of Endodontics: Exploring the Potential Role of Explainable Artificial Intelligence in Risk Stratification and Decision‐Making in Endodontics

Abstract

The incorporation of artificial intelligence (AI) into endodontics has predominantly focused on augmenting diagnostic accuracy, particularly in pinpointing periradicular radiolucencies and delineating the complex architecture of root canal systems via radiographic imaging and cone beam computed tomography (CBCT). These advancements represent a pivotal development within dental practice [1]; however, they merely scratch the surface of AI's broader potential to revolutionise the field of dentistry. The decision-making process in endodontic clinical practice is multifaceted, involving a myriad of factors that extend beyond diagnostic capabilities. Clinicians are tasked with integrating a thorough understanding of the patient's dental and medical history, recognising the distinct anatomical variations presented by each tooth, evaluating the implications of previous dental interventions, and formulating coherent treatment plans based on the preferences of the patient. In this nuanced landscape, the advent of Explainable Artificial Intelligence (XAI) emerges as a crucial development, fostering transparency and trust in AI-assisted workflows and paving the way for its broader acceptance and implementation in clinical settings. Despite the impressive predictive performance of numerous deep learning models, a central characteristic of these systems is their ‘black box’ nature. They generate outputs without elucidating the underlying mechanisms driving these conclusions. This lack of transparency presents significant ethical and clinical dilemmas for healthcare professionals. Clinicians may be reluctant to adopt AI technologies that fail to provide comprehensible justifications for their recommendations, particularly as societal expectations shift towards greater accountability and clarity in AI-generated medical decisions [2]. Furthermore, the expansion of regulatory frameworks aimed at enforcing ethical standards in healthcare underscores the imperative for transparency. This shift reinforces the notion that AI tools must not only achieve high accuracy but also be interpretable and justifiable to both healthcare practitioners and patients. Explainable AI (XAI) refers to a suite of machine learning techniques designed to illuminate the decision-making processes behind AI models. Unlike traditional models that may output a single prediction, XAI systems are engineered to provide insights into which input variables significantly influenced their conclusions [3]. In the context of endodontics, for instance, an XAI framework could identify that a recommendation for root canal retreatment is heavily influenced by a cluster of factors, including lesion size, the existence of missed root canals, the failure of coronal restorations, the specific type of tooth being treated, and the overall medical status of the patient. This level of transparency not only enhances clinical decision-making but also fosters improved communication between clinicians and patients, ultimately leading to more informed consent and shared decision-making. XAI is currently being utilised effectively across various medical specialties, such as oncology, cardiology, and critical care. In these areas, it plays a crucial role by improving risk stratification—helping healthcare professionals identify patients at higher risk for adverse outcomes—and enhancing decision support systems, which aid clinicians in making more informed choices tailored to individual patient needs. While the integration of XAI in dentistry, particularly in endodontics, is still largely aspirational, its application is increasingly feasible, which may hold the promise of transforming treatment planning, diagnosis, and patient management in dental practices, allowing for more precise interventions and improved patient outcomes. Oncology: XAI has facilitated the stratification of cancer patients by integrating multiple data points, such as tumour biology, treatment responses, and survival probabilities, into interpretable models [5]. Cardiology: Utilizing transparent machine learning models has proven effective in predicting major adverse cardiac events. The increased interpretability of these models not only enhances clinician confidence but also promotes compliance with AI-driven recommendations [6]. Critical Care and Radiology: Interpretable neural networks have been deployed to support critical triage decisions and prioritise diagnoses in emergency settings, unveiling the capability of XAI to enhance clinical workflows [7]. These successful implementations in broader medical fields provide a foundational framework for translating XAI's benefits into dental specialties, including endodontics. The evolution of XAI tools must strictly adhere to ethical standards that emphasize principles, such as fairness, transparency, and accountability [9]. There are several potential risks to consider, including biased training datasets, the underrepresentation of minority populations in AI algorithms, and various data security concerns that could compromise patient privacy. Regulatory agencies like the Food and Drug Administration (FDA) and European Medicines Agency (EMA) are actively developing guidelines for AI and machine learning-based software in healthcare; however, comprehensive frameworks specifically tailored to the field of dentistry are still in their infancy. Enhanced explainability serves as a crucial approach to address the complexities inherent in AI applications within healthcare. It significantly improves transparency and auditability, enabling clinicians to substantiate AI-driven recommendations, particularly in medico-legal scenarios. Consequently, XAI is integral to the development of ethically robust AI systems, ensuring that their decision-making processes can be effectively communicated and scrutinised. For XAI to effectively transform endodontic practices, educational platforms must evolve in tandem with technological advancements. Dental undergraduate (UG) curricula and postgraduate training (PGT) programs should incorporate essential competencies related to digital literacy, data interpretation, and the ethics surrounding AI in clinical settings [3]. This educational foundation is crucial for fostering the critical thinking skills necessary to interpret AI-generated explanations accurately and to apply them effectively within clinical scenarios [10]. Moreover, ongoing professional development (CPD) initiatives should be carefully designed to enhance the AI-related competencies of current clinicians. This will empower them to engage with AI outputs with confidence and responsibility. Ultimately, the capability to comprehend, challenge, or contextualise AI recommendations will become integral to evidence-based dentistry [8]. Integrating simulation-based learning, decision-support case scenarios, and interdisciplinary workshops involving data scientists into dental education will help facilitate this transformative shift toward the successful incorporation of XAI in endodontics. Explainable AI has the potential to advance endodontic practice, transforming clinical decision-making from a predominantly experience-based approach to a more data-informed and patient-centered approach. Though the current implementation of XAI in endodontics remains largely aspirational, successful applications in other medical fields serve as both an inspiration and a valuable roadmap for the specialty. To facilitate the effective translation of XAI into endodontics, it is essential to prioritise interdisciplinary collaboration and develop tools that are ethical, understandable, and validated through rigorous clinical testing. Such an approach will ensure that the integration of XAI aligns with both clinical needs and patient safety. Moreover, XAI may provide a significant shift toward a new educational paradigm within dentistry, emphasizing the need for clinicians and students to acquire AI literacy. This foundation will promote the responsible and efficient adoption of AI tools, thereby enriching the practice of endodontics as the profession enters an increasingly digital healthcare landscape. Ultimately, maintaining a focus on explainability, accountability, and clinical relevance is paramount in integrating AI technologies into clinical practice. The authors declare no conflicts of interest. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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