METHODS: Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts.
RESULTS: The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support.
CONCLUSION: This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.
METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices.
RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology.
CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.
METHODS: The study was done at the International Medical University, Malaysia, and the University of Lahore, Pakistan. Semi-structured interviews were conducted from 1st August 2021 to 17th March 2022. Demographic information was noted. Themes were identified, and a summary of the information under each theme was created.
RESULTS: A total of 24 (14 from Malaysia and 10 from Pakistan) faculty participated. Most agreed that undergraduate medical students can progress (at a differential rate) if they attain the required competencies. Among the major advantages mentioned were that students may graduate faster, learn at a pace comfortable to them, and develop an individualized learning pathway. Several logistical challenges must be overcome. Providing assessments on demand will be difficult. Significant regulatory hurdles were anticipated. Artificial intelligence (AI) can play an important role in creating an individualized learning pathway and supporting time-independent progression. The course may be (slightly) cheaper than a traditional one.
CONCLUSION: This study provides a foundation to further develop and strengthen flexible-length competency-based medical education modules. Further studies are required among educators at other medical schools and in other countries. Online learning and AI will play an important role.
METHODS: A comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified.
RESULTS: The top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P