This case report presents the concept of fabricating a 3D-printed prototype by outlining the benefits of these preliminary prosthetic constructions during implant treatment in a totally edentulous arch. Our study indicates that the final prosthetic construction anchored over dental implants demonstrates enhanced prosthetic features. Despite the advancements in digital dentistry, which offer significant diagnostic and planning capabilities, the fabrication of the final prosthetic construction remains a challenge even for specialists. This clinical case aims to present an in-house method for producing a 3D-printed prototype of the final prosthetic restoration, thereby minimizing clinical and laboratory errors such as incorrect selection of tooth form, size, and shade, improper occlusal contacts and articulation, suboptimal emergence profiles, and inadequate bridge design.

Artificial intelligence or as it is known in the English-language literature “AI” is broad term and is used inseveral fields of science. Artificial intelligence explores the possibility of computers performing tasks that would normally require human intelligence. This literature review describes the applications of different neural networks in dental medicine specifying the fields of dental implantology and prosthetic dental medicine. Neural networks are specific type of artificial intelligence and are used now a days in prosthodontic and dental implantology for enhancing the diagnostic process, creating treatment plans, generating 3D images and executing treatment plans. Creating 3D images is crucial for both branches of dentistry so one of the most sophisticated neural networks for generating such kind of images is well presented, the generative adversarial networks (GANs). Few limitations of this technology are also discussed. The purpose of this literature review is to investigate the different types of artificial intelligence and the purposes for their use in the domain of prosthetic dental medicine and dental implantology. Moreover, to establish the type of artificial intelligence with the highest performance for designing a 3D constructions.

This report presents a case of dental implant treatment with guided bone regeneration in a 46-year-old patient with active Crohn’s disease, who was undergoing long-term immunosuppressive therapy (azathioprine) and anti-inflammatory treatment (mesalazine). A titanium implant was placed six weeks after the extraction of tooth 22. Alveolar augmentation was performed using synthetic xenograft material in combination with a collagen membrane. Seven months later, a provisional restoration was delivered, followed by the placement of a definitive screw-retained crown 120 days afterward. No complications were reported throughout the treatment, and the patient consistently maintained excellent oral hygiene. This case underscores the feasibility of implant therapy in immunocompromised patients, despite longstanding concerns regarding the potential negative impact of immunosuppressive agents on bone metabolism and healing. Treatment success relied on a thorough medical history review, precise planning, minimally invasive surgical techniques, and the use of specific postoperative medications. Regular follow-up visits and diligent oral hygiene were also key to the favorable outcome. While this individual case was successful, further clinical studies are needed to establish comprehensive guidelines for implant treatment in patients with autoimmune conditions or organ transplants who are receiving immunosuppressive and systemic anti-inflammatory therapy.

Digital dentistry increasingly relies on artificial intelligence (AI) to automate restorative design. However, the ability of generative networks to produce multiple geometrically distinct outputs for the same prosthetic field remains insufficiently evaluated. This study assessed repeated-output geometric variability in a previously developed three-dimensional generative adversarial network (3DGAN) for screw-retained implant crown design as a preliminary indicator of potential generative diversity. Nine AI-generated implant crown designs were analyzed, consisting of three independently generated crowns for each of three different prosthetic fields. Within each set, the crowns were superimposed and compared using “MeshLab”. Mean Hausdorff distance (HD), maximum HD, and root mean square (RMS) values were recorded, with 0.05 model units used as the threshold for identifying insufficient morphological variation. The overall mean HD was 3.32 model units, the mean maximum HD was 16.18 model units, and the mean RMS value was 4.40 model units. No pairwise comparison showed values equal to or below 0.05 model units. In conclusion, the investigated 3DGAN demonstrated preliminary evidence of geometric output variability compatible with potential generative diversity.

Digital dentistry increasingly relies on artificial intelligence to automate restorative design. Yet the ability of generative networks to produce multiple clinically plausible solutions for the same prosthetic field remains insufficiently evaluated. This study assessed the creative potential of a previously developed three-dimensional generative adversarial network (3DGAN) for screw-retained implant crown design. Nine AI-generated implant crown designs were analyzed, consisting of three independently generated crowns for each of three different prosthetic fields. Within each set, the crowns were superimposed and compared using “MeshLab”. Mean HD, maximum HD, and RMS values were recorded, with 0.05 model units used as the threshold for identifying insufficient morphological variation. The overall mean HD was 3.32 model units, the mean maximum HD was 16.18 model units, and the mean RMS value was 4.40 model units. No pairwise comparison showed values equal to or below 0.05 model units. In conclusion we found that the investigated 3DGAN demonstrates real creative potential.