Revolutionising oral organoids with artificial intelligence. DOI
Jiawei Yang, Nicholas G. Fischer, Zhou Ye

et al.

PubMed, Journal Year: 2024, Volume and Issue: 5(4), P. 372 - 389

Published: Jan. 1, 2024

The convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional structures derived from human tissues offer invaluable insights into the complex biology diseases, allowing researchers effectively study disease mechanisms test therapeutic interventions in environments that closely mimic vivo conditions. In this review, we first present historical development organoids delve current types organoids, focusing on their use models, regeneration microbiome intervention. We then compare single-source multi-lineage assess latest progress bioprinted, vascularised neural-integrated organoids. next part highlight significant advancements AI, emphasising how AI algorithms may potentially promote for early detection diagnosis, personalised treatment, prediction drug screening. However, our main finding identification remaining challenges, such as data integration critical need rigorous validation ensure clinical reliability. Our viewpoint AI-enabled are still limited applications but, look future, potential transformation AI-integrated microbial interactions discoveries. By synthesising these components, review aims provide a comprehensive perspective state future implications role advancing healthcare improving patient outcomes.

Language: Английский

The current landscape of artificial intelligence in oral and maxillofacial surgery– a narrative review DOI
Rushil R. Dang,

Balram Kadaikal,

Sam El Abbadi

et al.

Oral and Maxillofacial Surgery, Journal Year: 2025, Volume and Issue: 29(1)

Published: Jan. 17, 2025

Language: Английский

Citations

1
Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications DOI Creative Commons
Vineet Vinay, Praveen Jodalli, Mahesh Chavan

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 280 - 280

Published: Jan. 24, 2025

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral diagnosis applications address a gap. Methods: A review identified, selected, synthesized AI-based diagnosis, screening, prognosis literature. The verified study quality relevance using frameworks inclusion criteria. full search included keywords, MeSH phrases, Pubmed. AI were tested through data extraction synthesis. Results: outperforms traditional analysis, prediction approaches. Medical pictures can be used diagnose with convolutional neural networks. Smartphone AI-enabled telemedicine make screening affordable accessible in resource-constrained areas. methods predict risk patient data. also arrange treatment histopathology images heterogeneity, restricted longitudinal research, clinical practice inclusion, ethical legal difficulties. Future potential includes uniform standards, long-term investigations, regulatory frameworks, healthcare professional training. Conclusions: may transform treatment. It develop early detection, modelling, imaging phenotypic change, prognosis. approaches should standardized, longitudinally, practical issues related real-world deployment addressed.

Language: Английский

Citations

1
Exploring a decade of deep learning in dentistry: A comprehensive mapping review DOI
Fatemeh Sohrabniya, Sahel Hassanzadeh-Samani,

Seyed AmirHossein Ourang

et al.

Clinical Oral Investigations, Journal Year: 2025, Volume and Issue: 29(2)

Published: Feb. 19, 2025

Language: Английский

Citations

1
Automatic Classification and Segmentation of Multiclass Jaw Lesions in Cone-Beam Computed Tomography using Deep Learning DOI
Wei Liu, Li Xiang, Chang Liu

et al.

Dentomaxillofacial Radiology, Journal Year: 2024, Volume and Issue: 53(7), P. 439 - 446

Published: June 27, 2024

To develop and validate a modified deep learning (DL) model based on nnU-Net for classifying segmenting five-class jaw lesions using cone-beam CT (CBCT).

Language: Английский

Citations

8
A Roadmap for the Rational Use of Biomarkers in Oral Disease Screening DOI Creative Commons
Nicola Cirillo

Biomolecules, Journal Year: 2024, Volume and Issue: 14(7), P. 787 - 787

Published: July 1, 2024

Oral health has witnessed a significant transformation with the integration of biomarkers in early-diagnostic processes. This article briefly reviews types used screening and early detection oral diseases, particularly cancer, periodontal dental caries, an emphasis on molecular biomarkers. While advent these may represent leap forward healthcare, it also opens door to potential overtesting, overdiagnosis, overtreatment. To inform selection novel ensure their rational use tests, is imperative consider some key characteristics, which are specific biomarker (e.g., surrogate should reliably reflect primary outcome), test sensitivity specificity must be balanced based disease interest), efficacy treatment improve when condition diagnosed earlier). For systemic conditions associated researchers extremely cautious determining who “at risk”, such risk small, non-existent, or inconsequent. framework aims that advancements diagnostics translate into genuine improvements patient care well-being.

Language: Английский

Citations

7
AI-Driven Diagnostics and Personalized Treatment Planning in Oral Oncology: Innovations and Future Directions DOI Creative Commons

R Satheeskumar

Oral Oncology Reports, Journal Year: 2024, Volume and Issue: unknown, P. 100704 - 100704

Published: Dec. 1, 2024

Language: Английский

Citations

4
Utilization of ChatGPT as a Reliable Aide for Differential Diagnosis of Histopathology in Head and Neck Surgery DOI Creative Commons

Sayyed Ourmazd Mohseni,

A R Saeid,

Patrick Wong

et al.

Oral Oncology Reports, Journal Year: 2025, Volume and Issue: unknown, P. 100727 - 100727

Published: Feb. 1, 2025

Language: Английский

Citations

0
Dense image-mask attention-guided transformer network for jaw lesions classification and segmentation in dental cone-beam computed tomography images DOI
Xiang Li, Wei Liu, Wei Tang

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: March 4, 2025

Language: Английский

Citations

0
Artificial intelligence in revolutionizing orthodontic practice DOI
Paul Fawaz, Patrick El Sayegh, Bart Vande Vannet

et al.

World Journal of Methodology, Journal Year: 2025, Volume and Issue: 15(3)

Published: March 6, 2025

This analytical research paper explores the transformative impact of artificial intelligence (AI) in orthodontics, with a focus on its objectives: Identifying current applications, evaluating benefits, addressing challenges, and projecting future developments. AI, subset computer science designed to simulate human intelligence, has seen rapid integration into orthodontic practice. The examines AI technologies such as machine learning, deep natural language processing, vision, robotics, which are increasingly used analyze patient data, assist diagnosis treatment planning, automate routine tasks, improve communication. systems offer precise malocclusion diagnoses, predict outcomes, customize plans by leveraging dental imagery. They also streamline image analysis, diagnostic accuracy, enhance engagement through personalized objectives include benefits terms efficiency, care, while acknowledging challenges like data quality, algorithm transparency, practical implementation. Despite these hurdles, presents promising prospects advanced imaging, predictive analytics, clinical decision-making. In conclusion, holds potential revolutionize practices improving operational precision outcomes. With collaborative efforts overcome could play pivotal role advancing care.

Language: Английский

Citations

0
Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis DOI Creative Commons
Baki Yılmaz, Büşra Yılmaz, Furkan Özbey

et al.

BMC Oral Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 15, 2025

Artificial intelligence (AI) has rapidly advanced in healthcare and dental education, significantly impacting diagnostic processes, treatment planning, academic training. The aim of this study is to evaluate the performance differences between different large language models (LLMs) by analyzing their accuracy rates answers multiple choice oral pathology questions. This evaluates eight LLMs (Gemini 1.5, Gemini 2, ChatGPT 4o, 4, o1, Copilot, Claude 3.5, Deepseek) answering multiple-choice questions from Turkish Dental Specialization Examination (DUS). A total 100 2012 2021 were analyzed. Questions classified as "case-based" or "knowledge-based". responses "correct" "incorrect" based on official answer keys. To prevent learning biases, no follow-up feedback provided after LLMs' responses. Significant observed among (p < 0.001). o1 achieved highest (96 correct, 4 incorrect), followed (84 correct), 2 Deepseek (82 correct each). Copilot had lowest (61 correct). Case-based showed notable variations = 0.034), where excelled. For knowledge-based questions, demonstrated Post-hoc analysis revealed that performed better than most other across both case-based 0.0031). variable proficiency with showing higher accuracy. shows promise a supplementary educational tool, though further validation required.

Language: Английский

Citations

0