Artificial Intelligence AI has transformed various sectors with healthcare and particularly dentistry emerging as key beneficiaries. In prosthodontics AI applications including machine learning deep learning and neural networks are streamlining diagnosis treatment planning and prosthesis fabrication. AI aids in designing custom-fit crowns bridges and dentures improving clinical outcomes and reducing chairside time. In implantology AI enhances surgical precision by analyzing CBCT images to guide implant placement minimizing complications. Studies have demonstrated AIs effectiveness in improving accuracy with some models achieving up to 97 success in tasks like crown margin identification and denture design. AI tools can also assess occlusion guide shade matching and enhance the casting process reducing manual errors. In removable prosthodontics convolutional neural networks CNNs have enabled accurate classification of partially edentulous arches and prediction of facial aesthetics. For maxillofacial prostheses AI supports non-surgical rehabilitation using smart devices enhancing color matching and comfort. Despite these advancements challenges persist. Limitations include a lack of data privacy insufficient practitioner training high implementation costs ethical concerns and incomplete standardization of AI algorithms. AI remains semi-automated relying heavily on high-quality datasets. Human expertise and clinical judgment remain irreplaceable. AI holds promising potential to enhance the quality efficiency and personalization of dental care. However its integration into clinical practice requires further empirical validation structured training modules and regulatory oversight. With responsible implementation AI can augment dental education and practice particularly in prosthodontics and oral implantology paving the way for a new era of precision and patient-centered care.
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V. Lavanya
Keerthivasan MS
Venkatakrishnan CJ
RGUHS Journal of Medical Sciences
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Lavanya et al. (Wed,) studied this question.
www.synapsesocial.com/papers/68c1b80c54b1d3bfb60eb976 — DOI: https://doi.org/10.26463/rjms.15_3_11