## 3 Discussion
The necessity of an interoperable digital data infrastructure for incorporating AI into healthcare is another emerging problem in the application of AI in periodontology and, subsequently, in medicine. Electronic health records (EHRs) must be easily accessible for AI development, but interoperability problems create obstacles that prevent fair progress. Strong regulatory monitoring, consistent investment, and proactive industry cooperation are necessary to maximize AI's influence in dentistry. AI-driven advancements will benefit patients, healthcare providers, and researchers equally if a safe, standardized digital architecture that facilitates smooth health data sharing is established [57]. This leads to another challenge to address, which is the flawed data in the development and implementation of AI models; in fact, even well-trained AI systems can produce erroneous outcomes if they are fed incorrect or biased input data. Focusing on data quality, enabling patient-initiated data corrections, and deploying automated error-correction systems could be key strategies to mitigate the risks associated with flawed data [54; 58]. Lastly, exploring hybrid approaches that combine AI diagnostics with decision-support systems could revolutionize treatment planning [59; 60]. In this direction, AI based on increasingly high-performance algorithms is highly connected to precision medicine, including in the the transostic and nanotechnology fields and in the field of biomedical imaging [61; 62; 63]. These systems could integrate multiple diagnostic parameters, such as radiographic findings, periodontal probing data, and patient risk factors, to provide comprehensive and personalized treatment recommendations. AI has proven to be powerful in periodonontology, offering unprecedented accuracy and efficiency in diagnosing and staging periodontal diseases. While challenges such as data imbalance, anatomical complexity, and reliance on 2D imaging persist, ongoing advancements in technology and research hold immense promise. By addressing these limitations and refining AI tools, periodonontology stands to benefit from the era of precision diagnostics and personalized care, ultimately improving patient outcomes and advancing dental medicine. The integration of AI in healthcare, particularly in managing sensitive patient information, raises several ethical and privacy considerations. Data privacy and security are paramount, as AI systems handle vast amounts of sensitive patient data. Healthcare providers must implement robust protection measures to prevent unauthorized access and breaches, ensuring patient confidentiality. Additionally, informed consent plays a crucial role in maintaining trust and transparency; patients must be fully aware of how their data will be used, including the potential benefits and risks associated with AI-driven healthcare solutions. Another critical issue is bias and fairness, as AI systems can inadvertently reflect biases present in the training data. To ensure equitable treatment for all patients, it is essential to use diverse datasets and continuously monitor AI systems for biased outcomes. Furthermore, accountability and transparency in AI-driven decision-making are vital. Healthcare providers must ensure that AI systems operate transparently, enabling clinicians to understand and validate AI-generated recommendations. This fosters trust and facilitates AI integration into clinical practice. Lastly, regulatory compliance is essential to ensure that AI applications align with ethical standards and data protection laws, promoting responsible and ethical AI use in healthcare. By addressing these ethical and privacy concerns, healthcare providers can effectively harness AI's potential to improve patient care while maintaining trust and safeguarding sensitive information.