Artificial Intelligence Helps with Insurance Verification and ADA Coding with Automation: A Change in the Dental Revenue Cycle

   

Dhara Shah¹*, Hardik Shah², Krisha Shah³, and Satya Upadhyayula⁴ 

Citation: Shah D, Shah H, Shah K, Upadhyayula S. Artificial Intelligence Helps with Insurance Verification and ADA Coding with Automation: A Change in the Dental Revenue Cycle.  J Oral Med and Dent Res. 6(3):1-4.

Received: November 19, 2025 | Published: December 05, 2025                                                 

Copyright©️ 2025 Genesis Pub by Shah D, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited.

DOI: https://doi.org/10.52793/JOMDR.2025.6(3)-106

Abstract

The ability of a device to carry out tasks often associated with human intellect, such as thinking, learning, and self-improvement, is known as artificial intelligence (AI) [1].  A branch of artificial intelligence called machine learning (ML) uses algorithms to discover inherent statistical patterns and structures to forecast unknown data. Deep learning (DL), inspired by the human brain's neurons, is one use of machine learning (ML) in medicine [2].  By passing data through multiple layers and filters, DL discovers hierarchical structure, learns from each input, and eventually maximizes accuracy and performance [3].  AI applications have expanded rapidly and influenced current healthcare research thanks to recent advances in computational power, data availability, and improved data processing tools.

Most time-consuming tasks in dentistry are insurance verification and accurate ADA coding. Any errors in these tasks can lead to claim denials, reduced reimbursement efficiency, staff burnout, and patient dissatisfaction. This article explains how Artificial Intelligence can help automate insurance verification and procedural coding.

AI systems can help standardize documentation, reduce human error, and speed reimbursement by using natural language processing (NLP) and machine learning. This article discusses how to integrate AI into dental practice to improve operations, clinical outcomes, Return on Investment, and implementation.

Keywords

Artificial Intelligence; Dentistry.

Introduction

Dental practices that offer an insurance plan to their patients are facing a significant administrative burden. This is mainly because of increased insurance complexities, heightened documentation requirements, and rising patient expectations for digital convenience. Initially, patient benefits and proper ADA (CDT) codes were determined manually, which consumed significant staff time, and any small documentation error often led to claim denials. Incorporating AI into these operational aspects of dentistry has enabled streamlining workflows and reducing errors [4].

Challenges in manual insurance verification

Manual insurance verification involves logging in to the individual insurance website, retrieving their insurance policy details, and understanding their benefits, which can take 15-30 minutes per patient. Doing these steps can lead to human error and misinterpretation. Any minor mistakes in insurance verification can result in an incorrect estimate for the patient, leading to bills and an upset patient.

How AI extracts and interprets benefits

An AI system, by harvesting data from digital platforms and using machine learning, can automate access to insurance portals and provide coverage information such as history, deductibles, coverage limits, frequency limits, and waiting periods. These data are then combined with the patient treatment plan in an interpretable format for all clinicians, administrative staff, and patients, improving consistency and clarity [5,6].

NLP for automated ADA coding

Natural language processing (NLP) and intelligent document processing (IDP) use AI to analyze speech and clinical notes. Digital data elements are combined to suggest or auto-assign appropriate ADA (CDT) codes [5,10]. Forbes stated in 2018 that the most important AI and AuI areas for healthcare would be improving administrative workflows, image reading and analysis, and virtual assistants [6].  All these advances will help reduce manual coding errors before the claim is submitted.

Impact on claim acceptance and reimbursement

Automated verification and linking to the correct ADA codes result in fewer claim denials and faster claim processing. This, in turn, increases the offices' cash flow through more claim documentation [4].

Implementation and training

Nowadays, there are many software products available on the market. Adopting AI for operations requires connecting the gap between AI and Practice management software. All staff members should be trained to make AI-generated suggestions. Though AI handles routine administrative work in a dental office, human oversight remains essential to address edge cases or unusual insurance plan provisions [8,9].

Risks, HIPAA consideration

When using AI platforms, we must ensure secure data transmission via an encrypted connection. Considering HIPAA rules, access control, and compliant vendor agreements is critical. Additionally, someone needs to check accuracy in complex or unusual cases [10,11].

Discussion

The use of artificial intelligence (AI) in processing dental claims marks a significant change in how oral healthcare systems handle administrative and clinical information. AI technologies can make claim submissions easier, reduce human errors, and provide more consistent decision-making for insurers and dental practices. By automatically checking radiographs, clinical notes, and treatment histories, AI systems can help verify whether a claim is complete, correctly coded, and supported by the appropriate documents. This automated validation can speed up processing times and lessen the administrative burden for both providers and payers.

One major benefit of using AI in dental claims is the potential for more objective and consistent results. Traditional claim reviews often rely on personal judgment, which can vary between reviewers. AI algorithms, trained on large datasets, can identify patterns related to billing issues, fraud, or insufficient clinical support. The effectiveness of these systems relies on the quality and diversity of their training data, since biases can lead to unfair or incorrect results.

While AI's potential in claims processing is encouraging, it also brings important ethical and practical challenges. One issue is transparency; AI decision-making is often seen as a "black box," making it hard for providers to understand the results. This can cause problems if clinicians feel that important details are missed. Furthermore, overreliance on automation can lead to new errors if human oversight is diminished. A balanced approach that combines automated evaluations with clinical judgment is crucial for successful implementation.

Clinical significance

Due to this inconsistency, current AI tools are not yet reliable enough to replace traditional methods. Instead, they should be considered as valuable supplementary aids in clinical practice. To fully harness the impact of AI in dental diagnostics, further refinement and rigorous validation are essential to ensure consistent and dependable applications in real-world clinical settings.

Conclusion

The use of artificial intelligence in processing dental claims could change administrative work in a big way. Automating tasks such as code validation, radiograph analysis, and documentation review can speed up processing, reduce human error, and make decisions fairer. However, these benefits can only be realized through the responsible implementation of AI tools, supported by strong data governance, clinician oversight, and an open system architecture. AI-driven claims processing is a promising step toward a more effective and patient-centered care system as the dental sector advances toward greater digital integration. Clinicians, insurers, and technologists must continue to work together to make sure that these tools support rather than replace professional judgment and ethical standards.

Limitations

Of all things, the quality, diversity, and completeness of the data used to train AI models are crucial factors. Claims approvals and denials may be influenced by routine mistakes arising from erroneous or biased datasets, leading to representations that do not accurately reflect real clinical needs. Second, AI systems may struggle to handle complex or uncommon cases that deviate from the patterns they learned during training. This might result in inaccurate claim processing, inadequate clinical note validation, or inaccurate radiograph interpretation. Third, because many AI models lack transparency, it is difficult for providers to understand the logic behind automated decisions. These mistakes have the potential to undermine trust and create obstacles in the appeals processes. AI may be challenging for smaller dental offices to implement because they have smaller IT teams that need employee training and also face significant financial responsibilities.

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