Intelligent system for clinical diagnoses of dental diseases using parallel programming in threads and automata in semantic validation as support for health specialists
Keywords:
Automated dental diagnosis, artificial intelligence in healthcare, parallel processing, clinical intelligent systems, semantic validation, radiographic analysis, digital oral healthAbstract
This project presents an innovative solution for automated dental diagnosis aimed at optimizing clinical accuracy, documentary traceability, and operational efficiency through structured symptomatic processing and semantic validation based on formal computational models. Its impact lies in reducing human error, standardizing clinical analysis, and strengthening the quality of diagnostic reports, consolidating an intelligent model applicable in real clinical environments where traditional diagnosis presents limitations derived from subjectivity, low replicability, and variability of criteria among professionals. These limitations affect clinical reliability, while artificial intelligence has proven to be an effective tool for symptomatic and radiographic interpretation, enabling the integration of clinical information into structured and auditable reports. In Colombia, the advancement of automated solutions for screening and clinical orientation demonstrates a favorable environment for the adoption of intelligent systems in oral health. The objective of the project is to develop an intelligent system for automated dental diagnosis, with technical traceability, structured validation, and parallel processing capable of correlating symptomatic descriptions and visual evidence through semantic validation algorithms and concurrent analysis. The methodology was structured into five phases within the Scrum agile framework: analysis of clinical–computational requirements, development of the symptomatic module, design of the diagnostic interface, implementation of export and documentary auditing components, and simulated validation using clinical criteria. The data used were obtained from structured scientific sources, clinical symptomatic datasets, and specialist- validated radiographic simulations, ensuring representativeness and technical coherence. The results show a system capable of processing symptoms expressed in natural language, correlating them with images, and generating structured diagnostic reports in PDF format, achieving diagnostic concordance above 92%, average response times below three seconds per thread, and complete semantic validation. In conclusion, the system demonstrates high technical and clinical viability, offering a robust, scalable tool aligned with national and international digital transformation policies in oral health.
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