Artificial intelligence (AI) refers to the ability of machines to exhibit a form of intelligence (1). AI is described as “a branch of science and engineering concerned with the computational understanding of what is often referred to as intelligent behavior and the development of artifacts that display such behavior” (2). At present, AI has brought a new paradigm that affects various disciplines, including science and technology, and affects everyday life (3). AI uses machines to mimic human intellectual behavior and cognitive skills like problem-solving (1, 4). The key aspects of AI include machine learning (ML), neural networks (NNs), and deep learning (DL), as illustrated in Figure 1 (1). The wide applications of these materials include information, construction, biomedicine, and biomaterials (3). AI has made it possible to analyze large amounts of data (big data) in real time and provides forecasts that can support the clinician's decisions (5).

ML is part of AI, and it depends on algorithms that can predict outcomes from datasets. It is based on algorithms trained for decision-making that robotically learn and recognize patterns from data. ML facilitates machine learning from data, and it can resolve issues/problems without people's involvement (1). DL is a constituent of ML that involves algorithms that are inspired by the structure and function of the human brain's NNs (6). DL uses deep NNs, which are composed of multiple layers of interconnected nodes, and DL constructs NNs to identify patterns to improve feature detection (1, 6). DL uses convolutional neural networks (CNNs), which is an automated feature discovery from raw data, resulting in better generalization and real-time decision-making. Hence, DL is increasingly important in medical and dental research, particularly in areas such as radiological image classification and segmentation, brain mapping with fMRI data, and diagnostic prognostication using various data types. The goals of AI research are reasoning, knowledge, planning, learning, natural language processing, perception, and moving and manipulating objects (7). Such technologies require good medical image processing of digital data, effective interpreting of diagnostic images, and applying mathematical operations for calculation and interpretation (8).

AI has led to wide applications in the medical sciences and education (9–11). The use of digital dentistry and dental computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies are being used in dental education in dental education curriculum (12). Nassani et al. (13) assessed the dental students' perception of digital technologies and CAD/CAM technologies integrating the dental students in scanning, designing, and manufacturing CAD provisional fixed dental restorations. They concluded that the presence of digital technology in practice and in educational academic environments significantly improved students' interest and perception of their knowledge and skills.

AI has been used in medical science. AI in imaging is one of the most developed areas for detection, classification, and splitting tasks in computer vision (14). AI is extensively utilized in medicine, where it serves a crucial function across various domains, including diagnosis and treatment planning, clinical care, laboratory processes, virtual assistant, treatment prognosis, educational training, administrative tasks, and electronic data record (EDR) (15). Recent advancements in AI in medical sciences include multimodal deep learning fusion (MDLF), speech data detection, and neuromorphic computing. At present, MDLF techniques are used in disease detection and diagnosis (16). The MDLF techniques can enhance the capabilities of machine learning models which can result in improved accuracy (17). These techniques provide adequate complementary information from multi-modal medical images and aid in disease diagnosis.

There is an increasing tendency of aging society with more elderly populations globally with more neurological and psychiatric disorders. Hence, there is a challenge to provide adequate care for many affected individuals, their families, and caregivers. AI has helped to address these problems to some extent. The interaction of neuromorphic computing and neuroscience results in understanding the human brain's complexities and addressing neurological challenges in elderly people. The body images are fused by using Siamese convolutional neural network structure and the entropy of the images (16). Therefore, the DL models can emphasize discerning complex patterns and offer advantages over conventional machine learning approaches. Recently, speech data have also been considered valuable clinical data for the detection of various diseases (18). AI-based techniques can help to categorize the data based on underlying algorithms. Such data helps to provide information on the association with the progressive degeneration of brain cells and successive impacts on cognition, memory, and language abilities. Various neurological diseases can be prevented, which can ultimately improve oral health. Finally, the development of neuromorphic computing has led to a transformative framework for modeling neurological disorders in drug development and therapeutic interventions (6, 19).

With the rising need for health care services, the needs of the hospital are evolving from traditional service-based to internet and smart hospitals (Figure 2) (3). AI can be implemented in public health services in various ways. One good example is the digital health Quick Response (QR) code, which uses a color-coding system to detect COVID-19 and show the person's health conditions using mobile data (20). This QR code was adopted by various countries to prevent and control pandemic diseases (especially COVID-19) worldwide.

The aim of this review is to present an overview of AI, its various aspects, and its application in biomedicine, dentistry, and dental biomaterials focusing on restorative dentistry and prosthodontics. Articles on AI and its applications in medicine, dentistry and biomaterials were searched on PubMed, Google Scholar, Scopus, and ScienceDirect. Additional sources were also searched for additional articles and relevant articles were included in this review.

In dentistry, AI is used in various specialties, i.e., maxillofacial radiology, orthodontics, prosthodontics, dental implantology, etc. (14, 21, 22). Modern dentistry is progressing into AI-assisted data-driven and robot-assisted. Various AI technologies have been applied such as robot dental assistants in oral surgery, tooth arrangement, computer-assisted orthodontics, and material testing (23). Diagnosis of diseases mainly relies on radiologists' subjective assessment and it varies according to clinical experience (8). AI models can be used to diagnose dental caries, root fractures, maxillofacial cysts, salivary gland diseases, maxillary sinusitis, osteoporosis, cancerous lesions, lymph node metastasis, and alveolar bone loss (1, 24). AI computer algorithms have also been applied for computer-assisted diagnosis and treatment using computed tomography (CT) and electrocardiography (ECG) repeatedly without fatigue (3, 22). The AI program anticipates pathology or prognosis by prioritizing risk factors (25). The first AI-based medical product permitted by the Food and Drug Administration (FDA) is IDx-DR, which uses an AI-based model based on patient images to predict diabetic retinopathy (26). Furthermore, an AI-based medical smartphone application called SkinVision can precisely identify melanomas (27). AI can be used to detect various anatomical landmarks, such as the maxillary sinus, nasal cavity, and condyles, as shown in Figure 3.

In orthodontics, AI models can be used to detect the need for orthodontic treatments, predict orthodontic extractions, and perform cephalometric analysis. In endodontics, AI models can be used to locate the apical foramen, assess root morphologies, predict retreatment, predict periapical pathologies, and detect root fractures (24). The different applications of AI in dentistry are illustrated in Figure 4 (25). AI is also useful in biological age and sex identification (25, 28).

Furthermore, AI can be used to evaluate occlusal contacts and predict mandibular morphology (1). AI can also be used for the automatic segmentation of panoramic radiographs showing normal anatomy of the oral and maxillofacial area (Figure 5). In addition, AI is also used for tooth presentation in 3D view by AI systems and automatic crown and caries detection on periapical radiographs (Figure 6). Hence, dentists are increasingly relying on computer applications and artificial intelligence models for clinical decision-making (29, 30). This approach not only supports clinical decisions but also increases the precision, efficiency, and accuracy of treatment planning and oral rehabilitation (31).

Although AI holds great promise, it also faces several challenges and ethical considerations. AI in dentistry is rapidly exploring new uses of AI for electronic health records, image analysis, and prosthesis design (92). Atlas-based and statistical shape models are important in capturing the shape, appearance, and location of organs but still a challenge to capture inter-subject variability (8).

Despite a progressive improvement, automatic cephalometry cannot completely replace manual tracing (93) and AI has exceeded the recommended magnitude of error for most cephalometric landmarks (94). It was also found that the AI automatic landmarking on 3D CBCT is found to be less accurate compared to 2D x-rays. Hence, AI-driven radiographic landmarking tracing requires the final supervision and approval of an experienced orthodontist.

Furthermore, important considerations include instilling knowledge of the basic knowledge and application of AI, examining current and potential ethical practices, and discussing its limitations (92, 95). Dental professionals need to understand and acclimatize themselves to such advances for better oral healthcare delivery (96). Finally, AI requires careful governance similar to the governance of physician conduct. Regulatory guidelines are needed regarding safely implementing and assessing AI technology (97).

In dentistry, AI technologies can be used as an important tool for treatment planning, diagnosis, prediction of treatment outcomes, and patient-centered care. The integration and continuous improvement of AI has brought significant advancements in dentistry. Various AI-driven software are going through continuous upgrades and developments. The application of AI in dentistry has made promising progress and has great potential for wider clinical applications in the near future.

The future of AI in dentistry extends into dental education, where AI-powered tools offer interactive learning, virtual patient simulations, and personalized feedback (98). Such technologies can allow dental students to practice clinical skills in a controlled, digital environment, preparing them for real-world scenarios.

Although AI has wide applications in dentistry, dental procedures performed by machines without human interaction are not representative of clinical care (25). There is also a risk of fully applying AI in dentistry without human control. AI has shown various errors in analysis and diagnosis. Hence, to avoid such errors, the final evaluation should be done by an experienced dentist.

Human-to-human communication is very difficult to translate directly into computer language and coding (4). Finally, the computational complexity of generating and selecting an atlas is a big challenge. Data safety, security, and privacy are critical aspects of integrating AI into health care (36, 92). Further long-term studies are needed into the specific capabilities to fully integrate AI into clinical practice.

The application of AI-based systems in dentistry and dental biomaterials is continually increasing. Recent advancement in AI in medical and dental sciences includes multimodal deep learning fusion, speech data detection, and neuromorphic computing. In the future, with the help of generative AI, dentists can prepare patient-specific prostheses for specific oral conditions. The application of AI-based technologies in prosthodontics is desirable in various aspects from clinicians' and patients' points of view. In prosthodontics, they have a tangible influence on widening opportunities for clinicians as well as patients and can be used as an additional simple tool for assembling, handling, and establishing patient-related datasets to deliver individual, patient-centered, and personalized treatment. In orthodontics, AI has contributed to diagnosis, treatment planning, and clinical practice. AI has made important improvements in rational design and has accelerated the discovery of various biomaterials used in dentistry. At present, AI still cannot fully replace human experts and it can serve as an important component in clinical dentistry.