Browsing by Author "Alhussin, Argwan"

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Challenges faced when masking a single discoloured tooth - Part 1: aetiology and non-invasive management.
(2025-06) Aljanahi, May; Alhussin, Argwan; Elbishari, Haitham
Encountering a single discoloured tooth is a common occurrence in dentistry and it poses a significant concern affecting both aesthetic appearance of natural teeth and patient confidence. Management of tooth discolouration involves a wide variety of options and requires specific protocols for both the clinician and patient to achieve an aesthetic result. One of the toughest challenges in restorative dentistry is being able to mimic natural teeth. This review is the first of two articles that will broadly discuss the aetiology of discolouration and the challenges faced when masking a single discoloured tooth. It will also examine various approaches, encompassing the conservative options, such as scaling, microabrasion, air abrasion, vital and non-vital tooth whitening, and resin infiltration. By integrating current and clinical evidence, this review aims to identify the causes of single tooth discolouration, highlight the challenges/variables faced when masking discoloured teeth and appraise possible minimally invasive procedures.
Challenges faced when masking a single discoloured tooth - Part 2: indirect restoration procedures
(2025-07) Aljanahi, May; Alhussin, Argwan; Elbishari, Haitham
Purpose: The use of large language models (LLMs) in generative artificial intelligence (AI) is rapidly increasing in dentistry. However, their reliability is yet to be fully founded. This study aims to evaluate the diagnostic accuracy, clinical applicability, and patient education potential of LLMs in paediatric dentistry, by evaluating the responses of six LLMs: Google AI’s Gemini and Gemini Advanced, OpenAI’s ChatGPT-3.5, -4o and -4, and Microsoft’s Copilot. Methods: Ten open-type clinical questions, relevant to paediatric dentistry were posed to the LLMs. The responses were graded by two independent evaluators from 0 to 10 using a detailed rubric. After 4 weeks, answers were reevaluated to assess intra-evaluator reliability. Statistical comparisons used Friedman’s and Wilcoxon’s and Kruskal–Wallis tests to assess the model that provided the most comprehensive, accurate, explicit and relevant answers. Results: Variations of results were noted. Chat GPT 4 answers were scored as the best (average score 8.08), followed by the answers of Gemini Advanced (8.06), ChatGPT 4o (8.01), ChatGPT 3.5 (7.61), Gemini (7,32) and Copilot (5.41). Statistical analysis revealed that Chat GPT 4 outperformed all other LLMs, and the difference was statistically significant. Despite variations and different responses to the same queries, remarkable similarities were observed. Except for Copilot, all chatbots managed to achieve a score level above 6.5 on all queries. Conclusion: This study demonstrates the potential use of language models (LLMs) in supporting evidence-based paediatric dentistry. Nevertheless, they cannot be regarded as completely trustworthy. Dental professionals should critically use AI models as supportive tools and not as a substitute of overall scientific knowledge and critical thinking.