Natural language processing for clinical notes in dentistry: A systematic review

Pethani, Farhana; Dunn, Adam G.

doi:10.1016/j.jbi.2023.104282

Cited by 11 publications

(3 citation statements)

References 66 publications

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“…Since clinical notes frequently use informal, unstructured language that may not adhere to a standardized vocabulary or style, it is challenging for conventional rule-based systems to recognize and retrieve information precisely. One of the possible drawbacks is the potential for missing diagnostic codes, which could restrict how broadly the NLP-based approach could be applied [ 33 , 77 , 78 , 79 , 80 , 81 ]. This is also applicable to [ 35 ] even though their ML-based HIE data model displayed a higher AUC in contrast to EHR patient-level data, as there is still a chance that the results will not be generalizable due to a potential lack of data availability in various HIE systems.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Borna,

Maniaci,

Haider

et al. 2023

Healthcare

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Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Borna,

Maniaci,

Haider

et al. 2023

Healthcare

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show abstract

“…NLP enables machines to comprehend and generate human-like text and streamline tasks, such as patient history documentation and automated communication. This not only saves time for dental professionals but also ensures comprehensive patient records, contributing to more informed restorative dentistry decisions ( Pethani F et al, 2023 ).…”

Section: Ai Fundamentals In Dentistrymentioning

confidence: 99%

The Application of artificial intelligence in restorative Dentistry: A narrative review of current research

Arjumand

2024

The Saudi Dental Journal

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“…It enables AI systems to extract meaningful insights from unstructured text, which can aid in understanding a patient's medical history, treatment plans, and progress. NLP can also help automate documentation and extract valuable information from large volumes of text-based data [ 10 ].…”

Section: Reviewmentioning

confidence: 99%

A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment

Mahesh Batra,

Reche

2023

Cureus

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The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.

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Natural language processing for clinical notes in dentistry: A systematic review

Cited by 11 publications

References 66 publications

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

The Application of artificial intelligence in restorative Dentistry: A narrative review of current research

A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment

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