Artificial intelligence (AI) is transforming the way dental professionals approach diagnostics and treatment planning. With the ability to analyse complex imaging data faster and more accurately than ever before, AI is becoming an essential tool in modern dental practices. One of its most powerful applications is in the detection of common and complex dental pathologies—helping clinicians make confident, data-driven decisions with speed and precision.
Below are five of the most significant dental pathologies that AI diagnostics can reliably identify, contributing to better patient outcomes and more efficient workflows.
- Periapical Lesions
Periapical lesions, often associated with chronic infections or pulp necrosis, can be difficult to detect in their early stages using traditional radiography. AI-powered diagnostics can enhance visibility by analysing 2D and 3D imaging data from CBCT scans to detect subtle bone loss and radiolucencies at the apex of the tooth. Early identification allows for prompt endodontic treatment, reducing the risk of complications such as abscess formation or tooth loss.
- Dental Caries (Cavities)
AI systems are now highly capable of detecting caries in both enamel and dentin, including interproximal lesions that are often missed during visual exams or by the human eye in X-rays. By scanning digital radiographs and CBCT data, AI can mark early-stage decay before it becomes clinically obvious, enabling preventive treatment and minimally invasive care.
- Periodontal Bone Loss
Periodontitis remains one of the leading causes of tooth loss in adults, and early detection of bone resorption is critical for effective intervention. AI diagnostics can quantify bone levels around each tooth with high accuracy, track progression over time, and support diagnosis and staging of periodontal disease. This aids in planning scaling, root planing, and surgical treatments, while also improving patient understanding through visualised reports.
- Impacted Teeth
Impacted teeth, especially third molars (wisdom teeth), can cause crowding, pain, cysts, or damage to adjacent structures if left untreated. AI tools can automatically identify impacted or partially erupted teeth in CBCT scans, providing measurements related to angulation, eruption path, and proximity to vital anatomical structures like nerves and sinuses. This information helps dental professionals plan extractions or orthodontic strategies more safely.
- Cysts and Tumors
Detecting cystic lesions or early-stage tumors in the jaw and surrounding tissues is a critical responsibility in dental imaging. AI can assist by scanning for abnormal growth patterns, changes in bone density, and irregular margins that may indicate benign or malignant lesions. Early detection can lead to timely referrals, biopsies, or surgical intervention—potentially saving lives and preserving oral health.
AI in Practice: Diagnocat Leading the Way
As AI becomes more embedded in clinical practice, platforms like Diagnocat are revolutionising how dentists detect and diagnose oral pathologies. Diagnocat uses advanced deep learning algorithms to analyse 2D and 3D dental images—automatically detecting dozens of conditions, generating radiological reports, and providing clinicians with clear visual insights to support accurate diagnosis.
With multilingual support and compliance with GDPR and other international standards, Diagnocat is trusted by dental professionals across Europe and beyond to enhance diagnostic precision, reduce chair time, and improve communication with patients.
The Future of Diagnostic Dentistry
AI is not just a supporting tool—it’s becoming a diagnostic partner. As technology continues to evolve, the scope of what AI can detect will only expand, offering deeper insights and earlier detection of oral diseases. For dental professionals who want to stay ahead, integrating AI diagnostics into daily practice is no longer optional—it’s essential.
By leveraging AI, dental practices can provide better care, increase case acceptance, and operate with a new level of clinical confidence.
