Artificial Intelligence in Pulp Vitality Assessment: A Novel Approach to Diagnostic Decision-Making

Abstract

The advent of artificial intelligence (AI) is a groundbreaking technology in the field of dentistry and it is promising new opportunities to improve the precision of diagnosis and decision-making. A field that is of considerable clinical concern is pulp vitality assessment which is a foundation of endodontic diagnosis and treatment planning. The classical diagnostic techniques, such as thermal pulp, electric pulp, and laser Doppler flowmetry, and the more modern ones, such as laser Doppler flowmetry, are usually associated with issues of subjectivity, variability of patients, and limited accuracy. The new solution offered by AI-based methods is based on the use of machine learning and deep learning algorithms to process various clinical and imaging data, thus, allowing objective and reproducible assessment of pulp status. Combining multimodal data (radiograph and thermography, patient history) will enable AI to be more diagnostic and aid clinicians with real time data. Moreover, the technologies promise improvements in the diagnostic errors, treatment outcomes, and the way to predictive and preventive endodontics. Despite these benefits there are still challenges in the areas of data availability, algorithm transparency, and clinical integration. Before widespread adoption, ethical issues, especially those to do with data privacy and possible bias, need to be addressed. However, AI is a potentially useful paradigm shift in the area of pulp vitality testing, and it could significantly enhance the quality of diagnostic decisions in the field of endodontics.