Harnessing Artificial Intelligence to Address Oral Health Disparities

Elani, Hawazin W.; Giannobile, William V.

doi:10.1001/jamahealthforum.2024.0642

Cited by 3 publications

(1 citation statement)

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“…Additionally, limited understanding of training algorithms by patients or the public can hinder the trust and usage of artificial intelligence. Claude 3 Opus, due to its accessibility to the public, can partially alleviate patient distrust towards AI and its extensive data sources contribute to a more balanced performance in health equity [48][49][50] . Based on our research findings, we believe Claude 3 Opus holds value in assisting physicians in clinical practice, and due to its accessibility, it may also have broader potential applications in medical education and patient communication.…”

Section: Discussionmentioning

confidence: 99%

Exploring the analysis capabilities and clinical application potential of the Claude 3 Opus in different dermatologic images: the development of a large-scale multimodal model to assist in dermatology clinical practice (Preprint)

Huang,

Liu,

Zhang

et al. 2024

Preprint

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BACKGROUND Publicly available, accessible, and user-friendly artificial intelligence is expected to serve in medical processes. Claude3, a newly introduced large-scale multimodal model, has demonstrated significantly superior image analysis capabilities compared to other models in official tests. However, there is no research reporting the potential of Claude3 in medical image analysis. OBJECTIVE To explore the analysis capabilities and potential applications of Claude3 Opus on dermatologic images. METHODS Dermoscopy and dermatopathology images from textbooks were collected, and question templates for different types of diseases were designed for Claude3 Opus. Three dermatologists used a structured scoring system with five modules to evaluate Claude3 Opus' analysis of images based on recognition, description, completeness, diagnosis, and clinical application, with each module scored out of 5 and a total score of 25. RESULTS A total of 330 images were collected. Claude3 Opus scored highest in pigmented disorders dermoscopy images (18.65/25), followed by vascular disorders dermoscopy (15.97/25) and vascular disorders dermatopathology images (15.86/25). In pigmented disorders, its score in dermoscopy (18.65/25) was significantly higher than in dermatopathology images (14.54/25), but no such difference existed in vascular disorders. Among the five modules, Claude3 Opus' recognition score (3.65/5) was significantly higher than the other four modules. There was no difference between description score (3.14/5) and completeness score (3.22/5), but they were significantly higher than the diagnostic score (2.47/5). Claude3 Opus scored higher in malignant diseases than benign diseases, regardless of dermoscopy or dermatopathology images (all p-values <0.05), with no impact from different magnifications in dermatopathology images (p>0.05) and no impact from different number of evaluators. CONCLUSIONS Claude3 Opus exhibits strong recognition capabilities for dermatologic disease images, can accurately describe abnormalities in images completely, and shows high sensitivity to malignant diseases. Apart from medical assistance, Claude3 Opus could potentially be widely used in medical education and patient communication. CLINICALTRIAL no need

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Section: Discussionmentioning

confidence: 99%

Exploring the analysis capabilities and clinical application potential of the Claude 3 Opus in different dermatologic images: the development of a large-scale multimodal model to assist in dermatology clinical practice (Preprint)

Huang,

Liu,

Zhang

et al. 2024

Preprint

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Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches

Feher,

Tussie,

Giannobile

2024

Front. Artif. Intell.

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Artificial intelligence (AI) is increasingly applied across all disciplines of medicine, including dentistry. Oral health research is experiencing a rapidly increasing use of machine learning (ML), the branch of AI that identifies inherent patterns in data similarly to how humans learn. In contemporary clinical dentistry, ML supports computer-aided diagnostics, risk stratification, individual risk prediction, and decision support to ultimately improve clinical oral health care efficiency, outcomes, and reduce disparities. Further, ML is progressively used in dental and oral health research, from basic and translational science to clinical investigations. With an ML perspective, this review provides a comprehensive overview of how dental medicine leverages AI for diagnostic, prognostic, and generative tasks. The spectrum of available data modalities in dentistry and their compatibility with various methods of applied AI are presented. Finally, current challenges and limitations as well as future possibilities and considerations for AI application in dental medicine are summarized.

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Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Pham,

Teh,

Chatzopoulou

et al. 2024

Current Oncology

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Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

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Harnessing Artificial Intelligence to Address Oral Health Disparities

Abstract: This Viewpoint explores the unique attributes of dentistry that could leverage artificial intelligence for many improvements including greater health equity.

Cited by 3 publications

References 3 publications

Exploring the analysis capabilities and clinical application potential of the Claude 3 Opus in different dermatologic images: the development of a large-scale multimodal model to assist in dermatology clinical practice (Preprint)

Exploring the analysis capabilities and clinical application potential of the Claude 3 Opus in different dermatologic images: the development of a large-scale multimodal model to assist in dermatology clinical practice (Preprint)

Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

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