Role of Artificial Intelligence (AI) in Patient Education and Communication in Dentistry

Thorat, Vinayak; Rao, Prajakta; Joshi, Nilesh; Talreja, Prakash; Shetty, Anupa R

doi:10.7759/cureus.59799

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…Artificial intelligence capabilities in management approach considering the currently available imaging modalities [ 76 , 77 ] and treatment options [ 78 ] would best benefit the patient. Table 1 summarizes novel applications of AI in KCN management [ 79 - 94 ] .…”

Section: Resultsmentioning

confidence: 99%

Keratoconus: imaging modalities and management

Alqudah

2024

Med Hypothesis Discov Innov Ophthalmol

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Background: Keratoconus (KCN) is characterized by gradual thinning and steepening of the cornea, which can lead to significant vision problems owing to high astigmatism, corneal scarring, or even corneal perforation. The detection of KCN in its early stages is crucial for effective treatment. In this review, we describe current advances in the diagnosis and treatment of KCN. Methods: This narrative review focuses on recent advancements in the diagnosis and treatment of KCN, especially evolving approaches and strategies. To ensure the inclusion of the most recent literature, relevant publications discussing advanced imaging techniques and treatment options for KCN were extensively gathered from the PubMed/MEDLINE and Google Scholar databases. The following index terms and keywords were used for the online search: keratoconus, diagnosis of keratoconus, advances in the diagnosis of keratoconus, topography or tomography, anterior segment optical coherence tomography, treatment of keratoconus, advances in the treatment of keratoconus, collagen crosslinking, intrastromal ring, keratoplasty, and new techniques in keratoconus. Results: Various screening methods such as corneal topography, tomography, anterior segment optical coherence tomography, and assessment of corneal biomechanics have been developed to identify KCN in its early stages. After diagnosis, KCN management focuses on preventing disease progression. Corneal collagen crosslinking is a minimally invasive treatment that can slow or stop the progression of the condition. Recent research has also explored the use of copper sulfate eye drops (IVMED-80) as a noninvasive treatment to prevent the progression of KCN. Current treatment options for visual improvement include scleral lenses, intracorneal ring segments, corneal allogeneic intrastromal ring segments, and deep anterior lamellar keratoplasty. Recently, novel alternative procedures, such as isolated Bowman layer transplantation, either as a corneal stromal inlay or onlay, have demonstrated encouraging outcomes. Artificial intelligence has gained acceptance for providing best practices for the diagnosis and management of KCN, and the science of its application is contentiously debated; however, it may not have been sufficiently developed. Conclusions: Early detection and advancements in screening methods using current imaging modalities have improved diagnosis of KCN. Improvement in the accuracy of current screening or diagnostic tests and comparison of their validities are achievable by well-designed, large-scale, prospective studies. The safety and effectiveness of emerging treatments for KCN are currently being investigated. There is an ongoing need for studies to track progress and evaluate clinicians’ knowledge and practices in treating patients with KCN. Artificial intelligence capabilities in management approach considering the currently available imaging modalities and treatment options would best benefit the patient.

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Section: Resultsmentioning

confidence: 99%

Keratoconus: imaging modalities and management

Alqudah

2024

Med Hypothesis Discov Innov Ophthalmol

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Exploration on the Integration of Artificial Intelligence to the Teaching of Periodontology

陈

2024

CES

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Digital Transformation in Healthcare - A Recent Progress and Applications of Artificial Intelligence in Healthcare (Preprint)

Nadeem,

Rashid,

Zafar

et al. 2024

Preprint

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UNSTRUCTURED Artificial Intelligence (AI) is revolutionizing various healthcare sectors, enhancing diagnostic accuracy, patient care, and operational efficiency. However, there is a need for more involvement from healthcare stakeholders in promoting patient access, care, and compliance with digital therapeutics. This study aims to comprehensively analyze AI applications across different healthcare settings, including hospitals, dentistry, physical therapy, pharmacies, nursing, and medical laboratories. The study conducted a thorough literature review, using keywords to examine relevant articles from PubMed/Medline, Scopus, and Google Scholar databases. The review content is not restricted by time but is limited to English-language articles. The study found a significant need for AI facilitation in dentistry, nursing, and midwifery. AI solutions tailored to these fields can benefit patient outcomes, streamline workflows, and reduce the burden on healthcare providers. The study also explores the extensive use of AI in advanced healthcare fields such as diagnostics, genetics, and forensics. It then compares AI with other digital health technologies in terms of data utilization, integration, clinical support decisions, and impact on patient care. Finally, it outlines future aspects that underscore the need for customized training programs, infrastructure development, and robust ethical and legal frameworks to guide the adoption of AI in healthcare settings, ensuring informed consent, patient privacy and data security.

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Role of Artificial Intelligence (AI) in Patient Education and Communication in Dentistry

Cited by 7 publications

References 28 publications

Keratoconus: imaging modalities and management

Keratoconus: imaging modalities and management

Exploration on the Integration of Artificial Intelligence to the Teaching of Periodontology

Digital Transformation in Healthcare - A Recent Progress and Applications of Artificial Intelligence in Healthcare (Preprint)

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