AI-Driven Digital Prosthodontics – A Paradigm Shift in Prosthetic Rehabilitation

Background: Digital and AI-driven technologies are revolutionizing prosthodontics by enhancing precision, efficiency, and patient outcomes. The integration of artificial intelligence (AI) in prosthetic design and occlusal analysis is transforming conventional workflows. Objective: This study evaluates the effectiveness of AI-assisted prosthodontic workflows in comparison to traditional methods in terms of accuracy, time efficiency, and patient satisfaction. Materials and Methods: A total of 50 edentulous patients were included in a randomized controlled study. Group A (n=25) received AI-assisted digitally designed complete dentures, while Group B (n=25) received conventionally fabricated dentures. Prosthetic fit, occlusal accuracy, patient satisfaction, and fabrication time were assessed using objective and subjective measures. Results: AI-assisted dentures demonstrated superior accuracy in occlusal balance (p<0.05), reduced fabrication time by 40%, and had significantly higher patient satisfaction scores compared to conventional methods. Discussion: The findings suggest that AI-driven digital prosthodontics enhances treatment outcomes by improving precision and reducing human error. However, challenges such as cost and learning curves must be addressed for widespread adoption. Conclusion: AI-driven digital prosthodontics offers a promising approach to improving efficiency and patient outcomes in prosthetic rehabilitation. Future research should focus on cost-effectiveness and long-term clinical outcomes.

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Publication Details

Journal
Journal of Neonatal Surgery
Published
2025-03-26
DOI
https://doi.org/10.52783/jns.v14.2651
Citations
1
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
1.03
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article

AI-Driven Digital Prosthodontics – A Paradigm Shift in Prosthetic Rehabilitation

Chandan Sengupta, Atharva D Kamble, Rohit Kumar Singh, Aishwarya Rajan Kamat et al.
1 citations
Journal of Neonatal Surgery
Stroke Rehabilitation and Recovery
1.03
article

AI-Driven Digital Prosthodontics – A Paradigm Shift in Prosthetic Rehabilitation

Chandan Sengupta, Atharva D Kamble, Rohit Kumar Singh, Aishwarya Rajan Kamat, Krishna Gorde, Nikhil Ajabrao Bomble
article en
1 citations

Abstract

Background: Digital and AI-driven technologies are revolutionizing prosthodontics by enhancing precision, efficiency, and patient outcomes. The integration of artificial intelligence (AI) in prosthetic design and occlusal analysis is transforming conventional workflows. Objective: This study evaluates the effectiveness of AI-assisted prosthodontic workflows in comparison to traditional methods in terms of accuracy, time efficiency, and patient satisfaction. Materials and Methods: A total of 50 edentulous patients were included in a randomized controlled study. Group A (n=25) received AI-assisted digitally designed complete dentures, while Group B (n=25) received conventionally fabricated dentures. Prosthetic fit, occlusal accuracy, patient satisfaction, and fabrication time were assessed using objective and subjective measures. Results: AI-assisted dentures demonstrated superior accuracy in occlusal balance (p<0.05), reduced fabrication time by 40%, and had significantly higher patient satisfaction scores compared to conventional methods. Discussion: The findings suggest that AI-driven digital prosthodontics enhances treatment outcomes by improving precision and reducing human error. However, challenges such as cost and learning curves must be addressed for widespread adoption. Conclusion: AI-driven digital prosthodontics offers a promising approach to improving efficiency and patient outcomes in prosthetic rehabilitation. Future research should focus on cost-effectiveness and long-term clinical outcomes.

Journal of Neonatal SurgeryVol. 14(9S)
Openalex Percentile: Top 24%
Stroke Rehabilitation and Recovery
1.03
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AI-Driven Digital Prosthodontics – A Paradigm Shift in Prosthetic Rehabilitation — Chandan Sengupta, Atharva D Kamble, et al. · Journal of Neonatal Surgery (2025) | TGRS Research Map | TGRS