The advancement of artificial intelligence in oral healthcare relies on the availability of large-scale multimodal datasets that capture the complexity of clinical practice. In this paper, we present a comprehensive multimodal dataset, comprising 8775 dental checkups from 4800 patients collected over eight years (2018-2025), with patients ranging from 10 to 90 years of age. The dataset includes 50000 intraoral images, 8056 radiographs, and detailed textual records, including diagnoses, treatment plans, and follow-up notes. The data were collected under standard ethical guidelines and annotated for benchmarking. To demonstrate its utility, we fine-tuned state-of-the-art large vision-language models, Qwen-VL 3B and 7B, and evaluated them on two tasks: classification of six oro-dental anomalies and generation of complete diagnostic reports from multimodal inputs. We compared the fine-tuned models with their base counterparts and GPT-4o. The fine-tuned models achieved substantial gains over these baselines, validating the dataset and underscoring its effectiveness in advancing AI-driven oro-dental healthcare solutions. The dataset is publicly available, providing an essential resource for future research in AI dentistry.

Oral diseases remain one of the most pervasive global health issues, affecting over 3.5 billion individuals, which accounts for over 43% of the global population as reported by the World Health Organization [1]. This widespread prevalence underscores the critical importance of dentistry, not only for clinical needs but also for enhancing the overall quality of life for a large portion of the global population. In modern dental practice, digital dentistry plays a crucial role in streamlining workflows and enhancing patient outcomes. Cone-Beam Computed Tomography (CBCT) visualizes 3D anatomical structures, including tooth morphology, alveolar bone, and surrounding tissues [2], while intraoral scans (IOS) provide high-resolution images of occlusal surfaces that are crucial for treatment planning and prosthesis design [3]. However, these imaging modalities still require extensive manual and time-consuming analysis to identify and plan treatments [4]. Consequently, numerous research efforts now focus on automating key tasks such as caries detection [5-7], orthodontic treatment planning [8-10], and designing dental prostheses, including implants, crowns, and bridges [11-13].

Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ 85259, USA Hexian Zhang: chordzhang@connect.hku.hk Tel: (852) 2852 0128, Fax: (852) 2548 9464 A bstract word count: 1 85 T otal word count: 31 67 T otal number of tables: 2 T otal number of figures: 3 N umber of references: 32 Keywords Artificial Intelligence, Deep Learning/Machine Learning, Dental Education, Electronic dental records, Periodontal Medicine Abstract Aims: Periodontology, with its wealth of structured clinical data, offers an ideal setting to evaluate the reasoning abilities of large language models (LLMs). This study aims to assess four LLMs (GPT - 4o, Gemini 2.0 Flash, Copilot, and DeepSeek V3) in interpreting longitudinal periodontal case vignettes through open - ended tasks. Materials and Methods: Thirty - four standardized longitudinal periodontal case vignettes were curated, generating 258 open - ended question - answer pairs. Each model was prompted to review case details and produce responses. Performance was evaluated using automated metrics (faithfulness, answer relevancy, readability) and blinded assessments by licensed dentists on a five - point Likert scale. Results: DeepSeek V3 achieved the highest median faithfulness score (0.528), outperforming GPT - 4o (0.457), Gemini 2.0 Flash (0.421), and Copilot (0.367).

Recent advances in large language models (LLMs) and medical LLMs (Med-LLMs) have demonstrated strong performance on general medical benchmarks. However, their capabilities in specialized medical fields, such as dentistry which require deeper domain-specific knowledge, remain underexplored due to the lack of targeted evaluation resources. In this paper, we introduce DentalBench, the first comprehensive bilingual benchmark designed to evaluate and advance LLMs in the dental domain. DentalBench consists of two main components: DentalQA, an English-Chinese question-answering (QA) benchmark with 36,597 questions spanning 4 tasks and 16 dental subfields; and DentalCorpus, a large-scale, high-quality corpus with 337.35 million tokens curated for dental domain adaptation, supporting both supervised fine-tuning (SFT) and retrieval-augmented generation (RAG). We evaluate 14 LLMs, covering proprietary, open-source, and medical-specific models, and reveal significant performance gaps across task types and languages. Further experiments with Qwen-2.5-3B demonstrate that domain adaptation substantially improves model performance, particularly on knowledge-intensive and terminology-focused tasks, and highlight the importance of domain-specific benchmarks for developing trustworthy and effective LLMs tailored to healthcare applications.

In this article, we present a new unique dataset for dental research - AlphaDent. This dataset is based on the DSLR camera photographs of the teeth of 295 patients and contains over 1200 images. The dataset is labeled for solving the instance segmentation problem and is divided into 9 classes. The article provides a detailed description of the dataset and the labeling format. The article also provides the details of the experiment on neural network training for the Instance Segmentation problem using this dataset. The results obtained show high quality of predictions. The dataset is published under an open license; and the training/inference code and model weights are also available under open licenses.

Recent advances in large language models (LLMs) have demonstrated notable performance in medical licensing exams. However, comprehensive evaluation of LLMs across various healthcare roles, particularly in high-stakes clinical scenarios, remains a challenge. Existing benchmarks are typically text-based, English-centric, and focus primarily on medicines, which limits their ability to assess broader healthcare knowledge and multimodal reasoning. To address these gaps, we introduce KokushiMD-10, the first multimodal benchmark constructed from ten Japanese national healthcare licensing exams. This benchmark spans multiple fields, including Medicine, Dentistry, Nursing, Pharmacy, and allied health professions. It contains over 11588 real exam questions, incorporating clinical images and expert-annotated rationales to evaluate both textual and visual reasoning. We benchmark over 30 state-of-the-art LLMs, including GPT-4o, Claude 3.5, and Gemini, across both text and image-based settings. Despite promising results, no model consistently meets passing thresholds across domains, highlighting the ongoing challenges in medical AI. KokushiMD-10 provides a comprehensive and linguistically grounded resource for evaluating and advancing reasoning-centric medical AI across multilingual and multimodal clinical tasks.

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 1 Haptic bilateral teleoperation system for free-hand dental procedures Lorenzo Pagliara, Student Member, IEEE, Enrico Ferrentino, Member, IEEE, Andrea Chiacchio, Giovanni Russo, Senior Member, IEEE Abstract --Free-hand dental procedures are typically repetitive, time-consuming and require high precision and manual dexterity. Dental robots can play a key role in improving procedural accuracy and safety, enhancing patient comfort, and reducing operator workload. However, robotic solutions for free-hand procedures remain limited or completely lacking, and their acceptance is still low. T o address this gap, we develop a haptic bilateral teleoperation system (HBTS) for free-hand dental procedures. The system includes a dedicated mechanical end-effector, compatible with standard clinical tools, and equipped with an endoscopic camera for improved visibility of the intervention site. By ensuring motion and force correspondence between the operator's actions and the robot's movements, monitored through visual feedback, we enhance the operator's sensory awareness and motor accuracy. Furthermore, recognizing the need to ensure procedural safety, we limit interaction forces by scaling the motion references provided to the admittance controller based solely on measured contact forces. This ensures effective force limitation in all contact states without requiring prior knowledge of the environment. The proposed HBTS is validated in a dental scaling procedure using a dental phantom. The results show that the system improves the naturalness, safety, and accuracy of teleoperation, highlighting its potential to enhance free-hand dental procedures. I NTRODUCTION A. Background R OBOTICS is rewriting the future of healthcare, emerging as a disruptive technology capable of optimizing and revolutionizing the way some medical procedures are conducted.

Artificial intelligence (AI) has become a commodity for people because of the advent of generative AI (GenAI) models that bridge the usability gap of AI by providing a natural language interface to interact with complex models. These GenAI models range from text generation - such as two-way chat systems - to the generation of image or video from textual descriptions input by a user. These advancements in AI have impacted Dentistry in multiple aspects. In dental education, the student now has the opportunity to solve a plethora of questions by only prompting a GenAI model and have the answer in a matter of seconds. GenAI models can help us deliver better patient healthcare by helping practitioners gather knowledge quickly and efficiently. Finally, GenAI can also be used in dental research, where the applications range from new drug discovery to assistance in academic writing. In this review, we first define GenAI models and describe their multiple generation modalities; then, we explain and discuss their current and potential applications in Dentistry; and finally, we describe the challenges these new technologies impose in our area.

Artificial intelligence has dramatically reshaped our interaction with digital technologies, ushering in an era where advancements in AI algorithms and Large Language Models (LLMs) have natural language processing (NLP) systems like ChatGPT. This study delves into the impact of cutting-edge LLMs, notably OpenAI's ChatGPT, on medical diagnostics, with a keen focus on the dental sector. Leveraging publicly accessible datasets, these models augment the diagnostic capabilities of medical professionals, streamline communication between patients and healthcare providers, and enhance the efficiency of clinical procedures. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. This paper sheds light on the current landscape and explores potential future research directions in the burgeoning field of LLMs, offering valuable insights for both practitioners and developers. Furthermore, it critically assesses the broad implications and challenges within various sectors, including academia and healthcare, thus mapping out an overview of AI's role in transforming dental diagnostics for enhanced patient care.

In medieval Europe, barber-surgeons might cut your hair, shave your face, do a bit of blood-letting and tend to a broken limb. They might also pull a tooth out with a "pelican" – a crude beak-like shank – or lever it out with an iron "tooth key". By the 17th century they might just knock it out with a steel punch elevator. It's a winding, gruesome road from these early practitioners of dentistry to today's world of 3D printing, artificial intelligence and robots that can create dental implants. Wayne Sampson, a dental historian and emeritus professor at the University of Adelaide, says the history of dental work goes back much further than the barber-surgeons.