Design Simulation and Assessment of Computer Based Cancer Diagnosis Accuracy using ART 1.0 Algorithm

Agrawal, Amit; Tiwari, Geeta

doi:10.17762/ijritcc.v10i3.5522

Cited by 7 publications

(1 citation statement)

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“…Cancer is not easily detected in its early stages. Thus, a computer-operated system that can distinguish between benign and malignant cells is used in diagnosis [ 11 ]. In the present study, more than 50% agreed that AI is used to assess the color of the lesion and analyze the histopathology of cancer.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence Among Dentists and Dental Students: A Cross-Sectional Online Survey

Kalaimani,

Chockalingam

et al. 2023

Cureus

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Background: Artificial intelligence (AI) is the process by which it is possible to program computers to mimic human thoughts. AI and its subsets machine learning and deep learning have been developed to analyze complicated data gathered from many sources using algorithms built into decision support systems. It has been widely used in the field of dentistry. Aim: The study aimed to evaluate the knowledge, attitude, and practice (KAP) of AI among dental students and dentists. Methodology: The present study is a descriptive cross-sectional online survey that was carried out among dentists and dental students in South India. A self-structured, close-ended questionnaire that was administered that consisted of 25 questions was included. The questions were circulated through Google Forms (Google LLC, Mountain View, California, United States), and it was circulated among the study participants through online mode. The data were collected systematically, and SPSS Statistics version 22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.) was used for data analysis. Results: One thousand (595 dental surgeons and 405 dental students) participated in the study through Google Forms. Among these, 700 (70%) were females and 300 (30%) were males. In the study group, 635 (63.5%) were aware of AI, and 365 (36.5%) were not aware (p-value 0.000). Among the 21 questions used to assess the KAP, 14 questions were significant with a p-value less than 0.05. More than 60% agreed that the dental curriculum has to be updated with AI. About 269 (26.9%) agreed that AI will replace the role of dentists in the future. There were no significant results in comparing dental surgeons and dental students. Conclusion: The present study showed that the KAP among dental surgeons and dental students was the same. They believe that the dental curriculum has to be updated with AI. This study shows that there is a lack of knowledge about deep learning models and websites used for AI among dentists. Thus, it is necessary to include evidence-based teaching and training about the application of AI in dental practice to improve the future of dentistry.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence Among Dentists and Dental Students: A Cross-Sectional Online Survey

Kalaimani,

Chockalingam

et al. 2023

Cureus

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Learning and prediction of cyber attack based on correlation mapping in classical optical networks

Alqahtani

2022

Opt Quant Electron

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Improved precision in detecting the early symptoms of COVID using k-nearest neighbor and support vector machine algorithm

Joseph,

Christy

2023

AIP Conference Proceedings

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Design Simulation and Assessment of Computer Based Cancer Diagnosis Accuracy using ART 1.0 Algorithm

Cited by 7 publications

References 0 publications

Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence Among Dentists and Dental Students: A Cross-Sectional Online Survey

Evaluation of Knowledge, Attitude, and Practice (KAP) of Artificial Intelligence Among Dentists and Dental Students: A Cross-Sectional Online Survey

Learning and prediction of cyber attack based on correlation mapping in classical optical networks

Improved precision in detecting the early symptoms of COVID using k-nearest neighbor and support vector machine algorithm

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