Global AI in Dentistry Market Size is valued at USD 421.0 Mn in 2024 and is predicted to reach USD 3,117.6 Mn by the year 2034 at a 22.3% CAGR during the forecast period for 2025 to 2034.
AI in Dentistry Market, Share & Trends Analysis Report, By Technology (Machine Learning, Natural Language Processing (NLP)), By Application (Dental Imaging and Diagnostics, Treatment Planning, Patient Management, Robotics and Automation, Predictive Analytics, Others), By End-user, By Region, and Segment Forecasts, 2025 to 2034
Artificial intelligence (AI) in dentistry refers to the use of computer algorithms and machine learning techniques to analyze dental data, assist in diagnosis, treatment planning, and improve overall patient care. AI applications in dentistry are rapidly evolving and have the potential to revolutionize various aspects of the field. AI algorithms can analyze dental X-rays and intraoral images to identify early signs of cavities, often more accurately than the human eye, allowing for timely intervention and prevention of more extensive damage. Additionally, AI can assess radiographs to evaluate bone loss and detect signs of gum disease, aiding in early diagnosis and management. AI is also being developed to analyze images of oral lesions to detect potentially malignant or pre-malignant conditions, significantly improving early detection rates and enhancing patient outcomes.
In December 2024, Pearl announced that its dental AI platform received a strategic investment from the American Dental Association (ADA), the country's leading dental organization. This investment reflects the ADA’s commitment to supporting innovative companies whose groundbreaking technologies enable dentists to enhance patient care and public health. The funding initiative was led by the ADA’s Innovation Advisory Committee, which is responsible for identifying and promoting advanced solutions that drive the future of dentistry. The growing adoption of artificial intelligence (AI) in dentistry is fueled by several key factors, including the increasing demand for early and accurate diagnosis of dental diseases, the rapid advancement of imaging technologies like digital radiography and 3D imaging, and the need for greater efficiency in dental practices through faster diagnoses, optimized treatment planning, and streamlined administrative tasks.
Some of the Major Key Players in the AI in Dentistry Market are:
The AI in Dentistry Market is segmented based on technology, application, cancer type, and end-user. Based on Technology, the market is segmented into therapeutic, machine learning, and natural language processing (NLP). Based on the Application, the market is divided into dental imaging and diagnostics, treatment planning, patient management, robotics and automation, predictive analytics, and others. Based on the end-user, the market is divided into dental practices & DSOs, diagnostic laboratories, and research institutions.
Based on Technology, the market is segmented into therapeutic, machine learning, and natural language processing (NLP). Among these, the machine learning segment is expected to have the highest growth rate during the forecast period. Machine Learning (ML) dominates the AI in Dentistry market because it is exceptionally well-suited for the types of data and tasks that are critical in dental care. First, dentistry heavily relies on visual data like X-rays, CBCT scans, intraoral images, and 3D models. ML, especially through techniques like deep learning and computer vision, excels at analyzing these images to detect cavities, bone loss, periodontal disease, and oral cancers with high accuracy. Machine Learning directly improves diagnostic precision, clinical decision-making, and operational efficiency, which are central to dental care. It holds a dominant position in the AI in Dentistry market.
Based on the Application, the market is divided into dental imaging and diagnostics, treatment planning, patient management, robotics and automation, predictive analytics, and others. Among these, the dental imaging and diagnostics segment dominates the market. AI tools, especially machine learning models, are the most advanced and widely adopted for analyzing dental images, significantly improving diagnostic speed, consistency, and accuracy. Regulatory clearances, such as FDA approvals, for AI in dentistry have mainly focused on imaging and diagnostics products, further boosting market trust and encouraging clinical adoption. Dental practices often prioritize investment in AI for imaging because it offers a quick return on investment through improved detection rates, enhanced workflow efficiency, and better patient outcomes.
The North America region particularly the United States, holds the largest share of the AI in Dentistry market during the forecast period. Clinics and hospitals in the U.S. and Canada are quick to adopt AI-based imaging, diagnostics, and treatment planning tools, driving market growth. Faster FDA approvals for AI-based dental products encourage early market entry and wider clinical use. Additionally, strong venture capital and institutional funding for dental AI startups and innovations further accelerates technological advancements. The growing emphasis on early diagnosis and personalized care also supports the widespread adoption of AI-driven solutions across the region.
| Report Attribute | Specifications |
| Market Size Value In 2024 | USD 421.0 Mn |
| Revenue Forecast In 2034 | USD 3,117.6 Mn |
| Growth Rate CAGR | CAGR of 22.3% from 2025 to 2034 |
| Quantitative Units | Representation of revenue in US$ Mn and CAGR from 2025 to 2034 |
| Historic Year | 2021 to 2024 |
| Forecast Year | 2025-2034 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Technology, Application, End-user |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; South East Asia |
| Competitive Landscape | Pearl, VideaHealth, Overjet, Denti.AI, Diagnocat, Dentem, ORCA Dental AI, 3Shape, DentBird,Planmeca, Carestream Dental, Neocis (Yomi robotic system), DeepCare, Bola AI, DentalMonitoring, SoftSmile, Brown Bacon AI, Apteryx Imaging (Planet DDS), Claronav Inc, Dental Wings (Straumann Group), Vatech Co., Ltd., ZimVie Inc, Adent, Toothfairy AI, Dentrix, Dexis, SmileMate, OrthoFX,K Line Europe, Others |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing and Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
Segmentation of AI in Dentistry Market
Global AI in Dentistry Market - By Technology
Global AI in Dentistry Market – By Application
Global AI in Dentistry Market – By End-user
Global AI in Dentistry Market – By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.