Published 17-04-2023
Keywords
- Dental treatment planning,
- AI algorithms,
- personalized medicine,,
- machine learning
How to Cite
Abstract
This paper explores the integration of AI-powered algorithms in the field of dentistry to enhance personalized treatment planning. Dental treatment planning traditionally relies heavily on clinician expertise and is often limited by subjective assessments. The proposed methodologies leverage AI to analyze patient data, including clinical records, imaging, and genetic information, to provide tailored treatment recommendations. This approach aims to improve treatment outcomes, patient satisfaction, and overall efficiency in dental care. The paper discusses various AI techniques, such as machine learning and deep learning, and their applications in dental treatment planning. Additionally, ethical considerations and challenges related to the implementation of AI in dentistry are addressed.
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References
- Khan, Mohammad Shahbaz, et al. "Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach." 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2023.
- Li, Xiaying, Belle Li, and Su-Je Cho. "Empowering Chinese Language Learners from Low-Income Families to Improve Their Chinese Writing with ChatGPT’s Assistance Afterschool." Languages 8.4 (2023): 238.
- Palle, Ranadeep Reddy. "Evolutionary Optimization Techniques in AI: Investigating Evolutionary Optimization Techniques and Their Application in Solving Optimization Problems in AI." Journal of Artificial Intelligence Research 3.1 (2023): 1-13.
- Veronin, Michael A., et al. "Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic." Drug, Healthcare and Patient Safety (2019): 65-70.
- Palle, Ranadeep Reddy. "Discuss the role of data analytics in extracting meaningful insights from social media data, influencing marketing strategies and user engagement." Journal of Artificial Intelligence and Machine Learning in Management 5.1 (2021): 64-69.
- Pillai, Aravind Sasidharan. "Multi-label chest X-ray classification via deep learning." arXiv preprint arXiv:2211.14929 (2022).
- Venigandla, Kamala, and Venkata Manoj Tatikonda. "Improving Diagnostic Imaging Analysis with RPA and Deep Learning Technologies." Power System Technology 45.4 (2021).
- Pillai, Aravind Sasidharan. "A Natural Language Processing Approach to Grouping Students by Shared Interests." Journal of Empirical Social Science Studies 6.1 (2022): 1-16.