Tooth-implant-supported removable dentures represent a controversial treatment modality because of the biomechanical differences between natural teeth and osseointegrated implants. This case report describes the prosthetic rehabilitation of an 85-year-old patient with Kennedy Class I mandibular edentulism using a tooth-implant-supported mandibular overdenture retained by two Locator® attachments over dental implants and one ball attachment over a natural mandibular canine.
Two dental implants were placed in the anterior mandible and restored with Locator® attachments, while the remaining mandibular right canine was prepared and restored with a custom cement-retained ball attachment. A mandibular acrylic overdenture was fabricated and retained using both implant- and tooth-supported attachments.
Clinical and radiographic follow-up evaluations performed at 12 and 24 months demonstrated stable peri-implant and periodontal conditions without detectable bone loss or signs of inflammation. The patient reported high satisfaction regarding denture retention, stability, function, and esthetics, and no mechanical complications were observed during the follow-up period.
Within the limitations of this case report, the combined use of Locator® and ball attachments for a tooth-implant-supported mandibular overdenture demonstrated favorable short-term clinical outcomes and high patient satisfaction. Nevertheless, further long-term clinical studies are required to validate the predictability and long-term success of this treatment approach.
Aleksandar Naydenov, Ilia Liondev, Rangel Todorov et al.· Problems of Dental Medicine· 0 citations
Generative artificial intelligence (AI) has emerged as a rapidly evolving technology with growing applications in healthcare education. Large language models, such as ChatGPT, Gemini, Claude and Microsoft Copilot, are increasingly being used to support learning, assessment, scientific writing and educational content development. In dental education, these technologies offer opportunities to enhance access to information, facilitate self-directed learning, provide personalized educational support, and improve student engagement.
This narrative review summarizes the current applications of generative AI in dental education and discusses its potential benefits, challenges, and future implications. Current evidence suggests that AI can support theoretical learning, clinical reasoning, assessment, feedback, and research-related activities. However, important concerns remain regarding the accuracy and reliability of AI-generated information, hallucinations, academic integrity, bias, data privacy, and excessive dependence on automated systems.
The successful integration of generative AI into dental curricula requires the development of AI literacy, ethical guidelines, and evidence-based implementation strategies. Although AI technologies have considerable potential to enhance educational processes, they should complement rather than replace educators, critical thinking, clinical reasoning, and hands-on clinical training. Continued research is needed to evaluate the long-term educational impact of AI and to establish best practices for its responsible use in dental education.
Ivet Dzhondrova, Dimitar Kirov· Problems of Dental Medicine· 0 citations
