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. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.
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http://dx.doi.org/10.1111/iej.14128 | DOI Listing |
J Med Imaging Radiat Oncol
December 2024
St John of God Subiaco, Perth, Western Australia, Australia.
Uterine leiomyomata, commonly known as fibroids, are prevalent benign tumours affecting a significant percentage of women of reproductive age. Although many patients remain asymptomatic, a substantial proportion experience severe symptoms, including abnormal uterine bleeding and adverse reproductive outcomes. Surgical intervention often becomes necessary for patients with symptomatic fibroids, despite advancements in medical therapies.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Imperial College Business School, Imperial College London, London, United Kingdom.
Background: In recent years, the adoption of large language model (LLM) applications, such as ChatGPT, has seen a significant surge, particularly among students. These artificial intelligence-driven tools offer unprecedented access to information and conversational assistance, which is reshaping the way students engage with academic content and manage the learning process. Despite the growing prevalence of LLMs and reliance on these technologies, there remains a notable gap in qualitative in-depth research examining the emotional and psychological effects of LLMs on users' mental well-being.
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October 2024
Department of Haematology, Institute of Clinical Pathology and Medical Research (ICPMR), Sydney Centres for Thrombosis and Haemostasis, Westmead Hospital, Westmead.
Ann Clin Transl Neurol
December 2024
MS Center Amsterdam, Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Objective: To assess the interrelationship between cortical lesions and cortical thinning and volume loss in people with multiple sclerosis within cortical networks, and how this relates to future cognition.
Methods: In this longitudinal study, 230 people with multiple sclerosis and 60 healthy controls underwent 3 Tesla MRI at baseline and neuropsychological assessment at baseline and 5-year follow-up. Cortical regions (N = 212) were divided into seven functional networks.
Int J Med Robot
February 2025
Insitute for Robotics and Kognitive Systems, University of Luebeck, Luebeck, Germany.
Background: Robotic ultrasound visualises internal organs in real-time for various medical applications without the harm of X-rays. The ultrasound probe is attached to the robot's end effector using custom-developed probe holders. This paper analyzes the impact of different probe holder geometries on the robot's base placement and reachability.
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