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      Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey

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          A BSTRACT

          Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists’ population of south Tamil Nadu region in India.

          Materials and Methods:

          A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis.

          Results:

          70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry.

          Conclusion:

          The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.

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          Physician Confidence in Artificial Intelligence: An Online Mobile Survey

          Songhee Oh, Jae Heon Kim, Sung-Woo Choi … (2019)
          Background It is expected that artificial intelligence (AI) will be used extensively in the medical field in the future. Objective The purpose of this study is to investigate the awareness of AI among Korean doctors and to assess physicians’ attitudes toward the medical application of AI. Methods We conducted an online survey composed of 11 closed-ended questions using Google Forms. The survey consisted of questions regarding the recognition of and attitudes toward AI, the development direction of AI in medicine, and the possible risks of using AI in the medical field. Results A total of 669 participants completed the survey. Only 40 (5.9%) answered that they had good familiarity with AI. However, most participants considered AI useful in the medical field (558/669, 83.4% agreement). The advantage of using AI was seen as the ability to analyze vast amounts of high-quality, clinically relevant data in real time. Respondents agreed that the area of medicine in which AI would be most useful is disease diagnosis (558/669, 83.4% agreement). One possible problem cited by the participants was that AI would not be able to assist in unexpected situations owing to inadequate information (196/669, 29.3%). Less than half of the participants(294/669, 43.9%) agreed that AI is diagnostically superior to human doctors. Only 237 (35.4%) answered that they agreed that AI could replace them in their jobs. Conclusions This study suggests that Korean doctors and medical students have favorable attitudes toward AI in the medical field. The majority of physicians surveyed believed that AI will not replace their roles in the future.
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            Deep Learning for the Radiographic Detection of Periodontal Bone Loss

            Joachim Krois, Thomas Ekert, Leonie Meinhold … (2019)
            We applied deep convolutional neural networks (CNNs) to detect periodontal bone loss (PBL) on panoramic dental radiographs. We synthesized a set of 2001 image segments from panoramic radiographs. Our reference test was the measured % of PBL. A deep feed-forward CNN was trained and validated via 10-times repeated group shuffling. Model architectures and hyperparameters were tuned using grid search. The final model was a seven-layer deep neural network, parameterized by a total number of 4,299,651 weights. For comparison, six dentists assessed the image segments for PBL. Averaged over 10 validation folds the mean (SD) classification accuracy of the CNN was 0.81 (0.02). Mean (SD) sensitivity and specificity were 0.81 (0.04), 0.81 (0.05), respectively. The mean (SD) accuracy of the dentists was 0.76 (0.06), but the CNN was not statistically significant superior compared to the examiners (p = 0.067/t-test). Mean sensitivity and specificity of the dentists was 0.92 (0.02) and 0.63 (0.14), respectively. A CNN trained on a limited amount of radiographic image segments showed at least similar discrimination ability as dentists for assessing PBL on panoramic radiographs. Dentists’ diagnostic efforts when using radiographs may be reduced by applying machine-learning based technologies.
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              A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology

              Jane Scheetz, Philip Rothschild, Myra B. McGuinness … (2021)
              Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Pharm Bioallied Sci
                Journal ID (iso-abbrev): J Pharm Bioallied Sci
                Journal ID (publisher-id): JPBS
                Journal ID (publisher-id): J Pharm Bioall Sci
                Title: Journal of Pharmacy & Bioallied Sciences
                Publisher: Wolters Kluwer - Medknow (India )
                ISSN (Print): 0976-4879
                ISSN (Electronic): 0975-7406
                Publication date (Print): April 2024
                Publication date (Electronic): 16 April 2024
                Volume: 16
                Issue: Suppl 2
                Pages: S1481-S1486
                Affiliations
                [1 ]Department of Orthodontics, Rajas Dental College and Hospitals, Kavalkinaru, Tamil Nadu, India
                [2 ]Department of Orthodontics, J.K.K. Nataraja Dental College and Hospitals, Nammakal, Tamil Nadu, India
                Author notes
                Address for correspondence: Dr. A. Jebilla Pringle, Department of Orthodontics, Rajas Dental College and Hospitals, Kavalkinaru - 627 105, Tamil Nadu, India. E-mail: jebillapringle@ 123456gmail.com
                Article
                Publisher ID: JPBS-16-1481
                DOI: 10.4103/jpbs.jpbs_1019_23
                PMC ID: 11174187
                PubMed ID: 38882768
                SO-VID: e93dc0fb-1ea3-4391-a456-7021ef41be68
                Copyright © Copyright: © 2024 Journal of Pharmacy and Bioallied Sciences
                License:

                This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

                History
                Date received : 10 October 2023
                Date revision received : 14 October 2023
                Date accepted : 22 October 2023
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: artificial intelligence,perception of ai among dentists,use of ai in dentistry
                Data availability:
                ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
                Keywords: artificial intelligence, perception of ai among dentists, use of ai in dentistry

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