Publications by authors named "Seyed AmirHossein Ourang"
Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions.
View Article and Find Full Text PDFThe integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field.
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- The study investigates the application of deep learning models in accurately diagnosing temporomandibular joint (TMJ) disorders, acknowledging ongoing challenges despite existing diagnostic criteria.
- A systematic review of literature was conducted across various platforms, ultimately including 21 studies that showed deep learning models with diagnostic accuracy ranging from 74% to 100%, indicating promising results for TMJ arthropathies.
- The findings suggest that these models demonstrate high sensitivity (95%) and specificity (92%), making them potentially valuable diagnostic tools for clinicians, especially those not specializing in orofacial pain.
Aim: This study aimed to evaluate and compare the validity and reliability of responses provided by GPT-3.5, Google Bard, and Bing to frequently asked questions (FAQs) in the field of endodontics.
Methodology: FAQs were formulated by expert endodontists (n = 10) and collected through GPT-3.
Objective: To evaluate the color match and color correlation between maxillary anterior teeth.
Materials And Methods: CIELab values of 1182 intact maxillary anterior teeth in 197 human specimens were measured through spectrophotometry. ∆E color differences between similar regions of the same and different type teeth were calculated and compared with perceptibility and acceptability thresholds using 1-sample t test to evaluate color matches.