Title : Machine learning in community oral health screening: The future of mass diagnostics
Abstract:
Oral diseases remain among the most prevalent non-communicable conditions worldwide, disproportionately affecting underserved populations. Conventional community oral health screening programs are constrained by workforce limitations, diagnostic subjectivity, logistical challenges, and delayed data processing. Machine Learning (ML) offers an innovative solution to enhance the efficiency, accuracy, and scalability of mass oral health diagnostics.
ML algorithms can analyze large datasets derived from intraoral images, electronic health records, salivary biomarkers, and behavioral risk indicators to identify early patterns of dental caries, periodontal disease, and other oral conditions. In school and community settings, AI-enabled mobile applications and cloud-based platforms enable rapid, standardized assessments, reducing inter-examiner variability and supporting real-time epidemiological surveillance.
This presentation highlights current applications of machine learning in community screening, including automated image-based caries detection, predictive risk modeling, and digital triage systems. Practical considerations such as algorithm validation, ethical governance, data privacy, cost-effectiveness, and integration into primary health care systems are critically discussed.
By shifting community screening from episodic detection to predictive, data-driven prevention, machine learning holds the potential to strengthen population-level oral health strategies and promote equitable access to early diagnosis and timely intervention.
Keywords: Machine Learning, Artificial Intelligence, Mass Screening; Oral Health, Community Health Services.
