ARTIFICIAL INTELLIGENCE'S EMERGING ROLE IN ORAL ONCOLOGY

被引:0
作者
Aggarwal, Dipanshu [1 ]
Shetty, Devi Charan [1 ]
机构
[1] ITS CDSR, Dept Oral Pathol & Microbiol, Ghaziabad, UP, India
来源
ACTA BIOCLINICA | 2022年 / 12卷 / 24期
关键词
AI (Artificial Intelligence); Attitudes; Awareness; Machine learning; Digital Pathology; Survey;
D O I
暂无
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
Artificial Intelligence (AI) and its application is providing new horizons in research and applied science. The goal of this study was to determine the level of awareness, attitudes, and future perspectives about AI among dental students and professionals and its application in digital pathology. A questionnaire survey was conducted among Undergraduate and Postgraduate dental students and Faculty/Clinicians through Google Forms. It was categorised into sections with the objective of determining knowledge, attitudes, and future perspectives of AI and its potential applications in pathology. The responders' identities were kept anonymous. A total of 200 people responded to the poll, with 136 females and 64 males with an average of 24 years. The study included 125 Undergraduates, 44 Post graduate students, and 31 Faculty/Clinicians. 73.5 % were aware that AI might be utilised in medicine. According to 87.5 %, it should be incorporated in the curriculum. 79 % feel it will play an important role in diagnostic and treatment planning in the future. Although the participants have a limited understanding of AI, they are eager to learn more about it. Participants expressed optimism, believing that AI will have a beneficial influence on medical practise in future. Artificial Intelligence approaches and development trends will focus on machine learning based on data acquired from the most recent diagnostic modalities, such as multi-omics (e.g., genomics, metabolomics) and imaging technologies, especially in areas where objective detection methods are missing. Finally, developing global and national standards and laws is required to accelerate the use and spread of AI in health and medicine. It will be useful in forecasting the prognosis, recurrence, and survival rate of oral cancer patients and also to predict the malignant transformation of pre-malignant lesion in high-risk patients.
引用
收藏
页数:229
相关论文
共 26 条
[1]   The impact of artificial intelligence in medicine on the future role of the physician [J].
Ahuja, Abhimanyu S. .
PEERJ, 2019, 7
[2]   Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool [J].
Alabi, Rasheed Omobolaji ;
Elmusrati, Mohammed ;
Sawazaki-Calone, Iris ;
Kowalski, Luiz Paulo ;
Haglund, Caj ;
Coletta, Ricardo D. ;
Makitie, Antti A. ;
Salo, Tuula ;
Leivo, Ilmo ;
Almangush, Alhadi .
VIRCHOWS ARCHIV, 2019, 475 (04) :489-497
[3]  
[Anonymous], 2018, Artificial Intelligence: How Knowledge is Created, Transferred, and Used: Trends in China, Europe and the United States
[4]  
[Anonymous], 2021, Journal of Contemporary Issues in Business and Government, V27, P3130
[5]   Development of a New Outcome Prediction Model in Early-stage Squamous Cell Carcinoma of the Oral Cavity Based on Histopathologic Parameters With Multivariate Analysis: The Aditi-Nuzhat Lymph-node Prediction Score (ANLPS) System [J].
Arora, Aditi ;
Husain, Nuzhat ;
Bansal, Ankur ;
Neyaz, Azfar ;
Jaiswal, Ritika ;
Jain, Kavitha ;
Chaturvedi, Arun ;
Anand, Nidhi ;
Malhotra, Kiranpreet ;
Shukla, Saumya .
AMERICAN JOURNAL OF SURGICAL PATHOLOGY, 2017, 41 (07) :950-960
[6]   Machine Learning for Clinical Chemists [J].
Badrick, Tony ;
Banfi, Giuseppe ;
Bietenbeck, Andreas ;
Cervinski, Mark A. ;
Loh, Tze Ping ;
Sikaris, Ken .
CLINICAL CHEMISTRY, 2019, 65 (11) :1350-1356
[7]   Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma [J].
Bur, Andres M. ;
Holcomb, Andrew ;
Goodwin, Sara ;
Woodroof, Janet ;
Karadaghy, Omar ;
Shnayder, Yelizaveta ;
Kakarala, Kiran ;
Brant, Jason ;
Shew, Matthew .
ORAL ONCOLOGY, 2019, 92 :20-25
[8]  
Chang HY, 2019, J PATHOL TRANSL MED, V53, P1
[9]   Computational Pathology to Discriminate Benign from Malignant Intraductal Proliferations of the Breast [J].
Dong, Fei ;
Irshad, Humayun ;
Oh, Eun-Yeong ;
Lerwill, Melinda F. ;
Brachtel, Elena F. ;
Jones, Nicholas C. ;
Knoblauch, Nicholas W. ;
Montaser-Kouhsari, Laleh ;
Johnson, Nicole B. ;
Rao, Luigi K. F. ;
Faulkner-Jones, Beverly ;
Wilbur, David C. ;
Schnitt, Stuart J. ;
Beck, Andrew H. .
PLOS ONE, 2014, 9 (12)
[10]   Medical students' attitude towards artificial intelligence: a multicentre survey [J].
dos Santos, D. Pinto ;
Giese, D. ;
Brodehl, S. ;
Chon, S. H. ;
Staab, W. ;
Kleinert, R. ;
Maintz, D. ;
Baessler, B. .
EUROPEAN RADIOLOGY, 2019, 29 (04) :1640-1646