Title

AI models for predicting long-term success of dental implants in medically compromised patients

Authors

Vansh Jaiswal^l, Dhwani Dave^2,*, Seema Rathi³, Vyankatesh Sahu⁴, JIgar M Yadav⁵ & Rinkee Mohanty⁶

Affiliation

¹Department of Periodontology, Narsinhbhai Patel Dental College and Hospital, Visnagar, Gujarat, India; ²Department of Dentistry, Goenka Research Institute of Dental Science, Piplaj, Gujarat, India; ³Department of Prosthodontics, Consultant Prosthodontist, Rohtak, Haryana, India; ⁴Department of Oral and Maxillofacial surgery Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital Hingna, Nagpur, India; ⁵Department Of Pediatric and Preventive Dentistry, Yogita Dental College and Hospital, Khed, Maharashtra, India; ⁶Department of Periodontics ,Institute of Dental Sciences, Siksha O Anusandhan deemed to be University, Bhubaneswar, India; *Corresponding author

Email

Vansh Jaiswal - E-mail: drvanshmds@gmail.com

Dhwani Dave - E-mail: dr.dhwanidave@gmail.com; DaveDhwani@grids.co.in

Seema Rathi - E-mail: seemaratheee@gmail.com

Vyankatesh Sahu - E-mail: vvmsahu@gmail.com

JIgar m yadav - E-mail: jigarmyadav@gmail.com

Rinkee Mohanty - E-mail: rinkeemohanty@soa.ac.in

Article Type

Research Article

Date

Received July 1, 2026; Revised July 31, 2026; Accepted July 31, 2026, Published July 31, 2026
 

Abstract

Predicting the long-term success of dental implants in medically compromised patients remains a major challenge in implant dentistry due to the influence of multiple systemic and local risk factors. This retrospective study analyzed data from 300 medically compromised patients to develop and evaluate artificial intelligence (AI)-based predictive models for implant outcomes using demographic, systemic and implant-related variables. Machine learning models, particularly the random forest algorithm, demonstrated higher predictive accuracy than conventional statistical methods. Systemic conditions, smoking status and bone quality were identified as significant predictors of implant success. Thus, data shows the AI-based predictive models can facilitate personalized risk assessment and enhance clinical decision-making in implant dentistry.

Keywords

Dental implants (DI); Artificial intelligence (AI); Machine learning (ML); medically compromised patients; implant success prediction

Citation

Jaiswal et al. Bioinformation 22(7): 3992-3995 (2026)

Edited by

Rashmi Laddha

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.