Perceptions on Ethical Dilemmas in Using Machine Learning for the Diagnosis of Oral Cancer among Dental Professionals

Abstract Background and Aim: Oral squamous cell carcinoma, a highly aggressive malignancy, accounts for over 90% of oral cancers globally and exhibits a high recurrence rate with poor survival outcomes in advanced stages. The integration of artificial intelligence, particularly machine learning (ML), holds promise for enhancing early detection and diagnosis, despite challenges in ethical considerations, data accessibility, and clinical application. The aim of the present study is to assess the Perceptions on ethical dilemmas in using ML for the diagnosis of Oral cancer among Dental professionals. Materials and Methods: The study, approved by the Institutional Review Board (Protocol No. YEC2/2024/307), involved 189 dental professionals (93 teaching faculty and 96 postgraduates) completing a validated 14-question survey on ethical dilemmas in using ML, analyzed using descriptive statistics. Results: The study achieved a 96.4% response rate, involving 189 participants–teaching faculty and postgraduates. Most agreed ML can aid early oral cancer diagnosis but raised concerns about misdiagnosis and ethical issues. Many supported ML’s role in improving access and efficiency, especially in rural areas, while emphasizing the need for professional training and human oversight. Conclusion: Our study highlights dental professionals’ optimism about ML in improving oral cancer diagnosis accuracy while emphasizing the need for ethical safeguards, transparency, and equitable access to ensure responsible and patient-centered integration into healthcare.