Using Supportive Dental AI Tools for Better Outcomes

Doctor Ekta Paydya, BDS, DDS writes in her Dental Economics article that treatment planning in dentistry is highly variable.  Clinicians are shaped by their training, experience, biases, available tools, and day-to-day pressures. This variability can reduce patient trust, lower case acceptance, and create operational challenges, especially in multi-provider practices and DSOs. She presents artificial intelligence (AI) as a supportive tool that can improve consistency by integrating clinical evidence, patient-specific factors, and radiographic findings into more structured treatment planning while keeping dentists accountable for final decisions.

Findings

• Treatment plans can vary significantly between dentists even when they review the same patient information.

• Clinical judgment is influenced by education, past experience, available knowledge, subjective bias, and the materials or methods a clinician has encountered.

• Operational pressures such as busy schedules, difficult patient interactions, and decision fatigue can further affect treatment decisions.

• Inconsistent recommendations create confusion for patients, weaken confidence, and may reduce case acceptance.

• Case acceptance is already a challenge, with established patients accepting at higher rates than new patients.

• Existing AI tools help with specific clinical tasks such as radiograph analysis, caries detection, bone-loss identification, periodontal charting, insurance verification, and clinical documentation.

• Current dental AI tools often operate in separate lanes and do not yet provide an integrated, comprehensive treatment-planning framework.

• A well-trained AI system could evaluate multiple clinical variables at once and offer evidence-based options more consistently than unaided human decision-making.

• AI can support confidence and consistency, but it should not replace dentist judgment or accountability.

Recommendations

• Use structured treatment-planning systems to reduce provider-to-provider variability.

• Adopt AI as a decision-support tool that helps organize patient data, radiographs, risk factors, and evidence-based options.

• Maintain dentist oversight and accountability for every final treatment decision.

• Move toward integrated AI platforms that connect diagnostic findings, periodontal data, restorative needs, medical history, photographs, and patient risk factors.

• Use AI-generated or AI-supported planning outputs to improve clarity, consistency, and patient confidence during case presentation.

• Train clinicians and teams to communicate treatment plans in a consistent, context-rich way rather than presenting disconnected procedures.

• Track case acceptance, scheduling consistency, and patient referrals to measure whether more structured planning improves practice performance.

• Continue using peer consultation and clinical judgment, while supplementing them with evidence-based AI support to reduce blind spots and bias.

Read Dr. Pandya's article here.