Deepfake: A Boon for Pediatric Dental Patients
Received: 4 June 2025 | Revised: 8 July 2025 and 20 July 2025 | Accepted: 23 July 2025 | Online: 6 October 2025
Corresponding author: Madhuri Tasgaonkar
Abstract
Deepfake refers to a technique that utilizes Generative Adversarial Networks (GANs), a type of Artificial Intelligence (AI), to generate synthetic visual content such as images and videos. Although commonly associated with misinformation, this technology also offers positive applications in domains including medical training, education, and imaging. However, its use in personalized behavioural management of pediatric dental patients remains unexplored. This study seeks to develop a real-time application leveraging deepfake technology to reduce dental anxiety in children. The application replaces the face of an anxious pediatric dental patient with an alternative face in a video, providing a personalized distraction during treatment. Three widely used tools for face swapping and deepfake generation—DeepFaceLab, Roop, and MobileFaceSwap—were evaluated, with their architectural characteristics, advantages, and limitations analysed in the context of this use case. A pilot study involving 20 anxious pediatric patients demonstrated that the application significantly reduced anxiety and fear associated with dental treatment.
Keywords:
Generative Adversarial Networks (GANs), deepfake, behavioural managementDownloads
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Copyright (c) 2025 Aditi Tasgaonkar, Madhuri Tasgaonkar, Rujuta Joshi, Harshada Chavan, Nilesh Rathi
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