## 7 Limitations and Challenges in AI Integration  
Despite the growing enthusiasm for AI in dental radiology, its widespread integration into clinical practice faces several technological, ethical, and operational barriers. These limitations can reduce accuracy, accessibility, and clinician trust in AI systems. The following Table 1 summarizes the most significant limitations and integration challenges, as identified in the current literature, and categorizes them for easier interpretation. Deep learning systems often lack transparency, making it difficult to interpret or justify their decisions, an issue that raises significant concerns in clinical settings. To address these risks, including automation bias and the black-box nature of AI, several regulatory and ethical frameworks have been introduced. The European Union's Artificial Intelligence Act (2021), for example, proposes a risk-based approach that requires transparency, human surveillance, and stability in healthcare AI applications [59]. Similarly, the U.S. Food and Drug Administration (FDA) has developed guidelines under its Software as a Medical Device (SaMD) framework, emphasizing quality assurance, validation, and continuous monitoring of AI tools [60, 61]. These initiatives aim to safeguard patient safety and ensure accountability across the AI lifecycle. Despite these efforts, implementation remains inconsistent, and widespread clinical adoption hinges on the availability of validated, interoperable systems and robust regulatory enforcement. This lack of uniformity introduces medico-legal and decision-making challenges, particularly in high-stakes diagnostic scenarios. For instance, convolutional neural networks (CNNs), despite their high diagnostic accuracy, operate through abstract feature extraction layers that are not easily interpretable by clinicians. In dental imaging, CNNs have demonstrated strong performance in detecting periapical lesions and periodontal bone loss; yet the reasoning behind specific outputs often remains opaque. This \begin{table}
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**Limitation/Challenge** & **Description** & **References** \\ \hline High Costs and Accessibility & Advanced systems like CBCT and AI platforms are expensive to acquire and maintain, limiting access in under-resourced settings. & [54, 55] \\ \hline Training and Maintenance & Clinicians require specialized training, and systems need regular updates and calibration to function effectively. & [54, 55] \\ \hline Radiation Exposure and Image Quality & Though improved, CBCT still poses radiation risks, requiring strict adherence to dose optimization protocols. & [56] \\ \hline Inconsistent Image Quality & Operator variability and poor equipment calibration can lead to inconsistent diagnostic image quality. & [56] \\ \hline Lack of Generalizability of AI Models (Algorithm Validation) & AI models trained on limited or non-diverse datasets perform poorly across varied demographics or imaging protocols. & [56, 57] \\ \hline Data Privacy and Security Risks & AI relies on large data volumes, raising concerns about patient consent, data breaches, and compliance with regulations. & [58, 59, 60] \\ \hline Automation Bias and Black-Box Models & Clinicians may over-rely on AI outputs, especially when decisions are not explainable, leading to diagnostic errors. & [61, 62] \\ \hline Lack of Explainability & Deep learning systems often lack transparency, making it hard to understand or justify AI decisions & [61] \\ \hline Insufficient External Validation & AI tools often lack real-world testing on varied datasets, limiting their applicability and performance consistency & [63] \\ \hline Lack of Standardized Guidelines & There are no unified standards for evaluating, certifying, or regulating AI tools in dental radiology & [63] \\ \hline Technical Barriers in Integration & Smaller clinics may lack IT infrastructure and staff to adopt and maintain AI tools effectively & [64] \\ \hline \hline \end{tabular}
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