Author(s):
Sudhir Rama Varma, Manjusha Nambiar, Raghad Hashim, Mohammed Amjed Alsaegh
Email(s):
s.varma@ajman.ac.ae
DOI:
10.52711/0974-360X.2023.00047 
Address:
Sudhir Rama Varma1,2*, Manjusha Nambiar3, Raghad Hashim4,2, Mohammed Amjed Alsaegh5
1Department of Clinical Sciences, Ajman University, Ajman, UAE.
2Center of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, UAE.
3Department of Periodontics, Sri Rajiv Gandhi College of Dental Sciences and Hospital, Benguluru 560032, Karnataka, India.
4Department of Basic Sciences, Ajman University, Ajman, UAE.
2Center of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, UAE.
5Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, UAE.
*Corresponding Author
Published In:
Volume - 16,
Issue - 1,
Year - 2023
ABSTRACT:
The advent of virtual clinical trials in dentistry has been progressing rapidly, especially during the COVID-19 pandemic. Most dental clinical research trial methodologies that were conventionally performed have given way to artificial intelligence and other augmented methods using digital platforms. The current review entails the imminent transformation that has taken over the dental clinical trial research. The review was conducted using the search engines: PubMed, PubMed Central, SciELO, Cochrane Database, and Clinicaltrials.gov. The search was carried out using keywords: "Virtual clinical trial,” "Dentistry,” "Tele-dentistry,” "Big Data,” "Augmented Reality,” "hybrid trial,” "Rapid Prototyping,” "3D,” "Artificial Intelligence,” "AI,” "Personalized dental Medicine,” "Institutional virtual trial" were selected. Various components of the trials, such as patient selection, informed consent, and remote monitoring, currently employ artificial intelligence or smart platforms. Though these novel digital interfaces are still in their early stages of application in dental research methodologies, they are not without limitations. In the long run, all stakeholders involved in the virtual clinical trials must provide patient-centered treatment outcomes and ethical treatment delivery.
Cite this article:
Sudhir Rama Varma, Manjusha Nambiar, Raghad Hashim, Mohammed Amjed Alsaegh.Digital Clinical Trials-Institutional Challenges in Dental Research. Research Journal of Pharmacy and Technology 2023; 16(1):255-0. doi: 10.52711/0974-360X.2023.00047
Cite(Electronic):
Sudhir Rama Varma, Manjusha Nambiar, Raghad Hashim, Mohammed Amjed Alsaegh.Digital Clinical Trials-Institutional Challenges in Dental Research. Research Journal of Pharmacy and Technology 2023; 16(1):255-0. doi: 10.52711/0974-360X.2023.00047 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2023-16-1-47
REFERENCES
1. Eilenberg K, Hoover A, Rutherford M, Melfi C, Segal S. From informed consent through database lock: an interactive clinical trial conducted using the Internet. Drug Inform J 2004;38(3):239–51.
2. Pfizer. Web-based methodology trial to evaluate the efficacy and safety of Tolterodine ER in subjects with overactive bladder (REMOTE) [Internet]. 2013. Available from: https://clinicaltrials. gov/ct2/show/NCT01302938. [Accessed on 25th July 2021]
3. National Academies of Sciences, Engineering, and Medicine 2019. Virtual clinical trials: challenges and opportunities: proceedings of a workshop. Washington, DC: The National Academies Press; 2019.
4. Nagarjan Tukuru, Shivalinge Gowda KP, Syed Mansoor Ahmed. Rapid Prototype Technique in Medical Field. Research J. Pharm. and Tech. 1(4): Oct.-Dec. 2008; Page 341-344.
5. Joda T, Ferrari, M, Gallucci, GO, Wittenben JG, Bragger U. Digital technology in fixed implant prosthodontics. Periodontology 2000 2017; 73, 178–192.
6. Pensieri, C.; Pennacchini, M. Overview: Virtual reality in medicine. J. Virtual Worlds Res. 2014, 7, 1–34.
7. Bharath B. C., Balamuralidhara V., M. P. Gowrav. Role of Real World Data and Real World Evidence in Regulatory Decision Making. Research J. Pharm. and Tech. 2021; 14(2):1177-1182.
8. Beryl Juliet V.S., Sudha M. Perception and Attitude of Staff Nurses towards Electronic Health Records.. Asian J. Nur. Edu. and Research 3(2): April.-June 2013; Page 93-97.
9. Joda, T.; Gallucci, G.O. The virtual patient in dental medicine. Clin. Oral Implant. Res. 2015, 26, 725–726.
10. Baethge, C., Goldbeck-Wood, S. & Mertens, S. SANRA—a scale for the quality assessment of narrative review articles. Res Integr Peer Rev 4, 5 (2019). https://doi.org/10.1186/s41073-019-0064-8.
11. Kotta Kranthi Kumar. Importance and Applications of Artificial Intelligence (Metastorm Software) in Pharmaceutical Process Life-Cycle. Res. J. Pharma. Dosage Forms and Tech.2019; 11(2):116-120.
12. RNSK Kartheek, V Rama Raju, R Md. Shafi. Blue Brain – A New Subway to Artificial Intelligence and Human Machine: A Proposal. Int. J. Tech. 4(2): July-Dec. 2014; Page 287-290
13. Ayoub A, Pulijala Y. The application of virtual reality and augmented reality in Oral & Maxillofacial Surgery. BMC Oral Health 2019; 19, 238.
14. Leite AF, Vasconcelos KF, Willems H, Jacobs R. Radiomics and machine learning in oral healthcare. Proteom Clin Appl 2020; e1900040, [Epub ahead of print].
15. Wang S, Parsons M, Stone-McLean J, Rogers P, Boyd S, Hoover K, Meruvis-Pastor O, Gong M, Smith A. Augmented reality as a telemedicine platform for remote procedural training. Sensors 2017; 17,2294.
16. Estai M, Bunt S, Kanagasingam Y, Tennant M. Cost savings from a teledentistry model for school dental screening: An Australian health system perspective. Aust. Health Rev 2018; 42, 482–490.
17. Joda T, Gallucci G.O. The virtual patient in dental medicine. Clin Oral Implant Res 2015; 26, 725–726.
18. Igna R, Stefan S, Onac I, Onac I, Ungur R, Szentagotai TA. Mindfulness-based cognitive-behavior therapy (MCBT versus virtual reality (VR) enhanced CBT, versus treatment as usual for chronic back pain. A clinical trial. J Evidence-Based Psychother 2014;14(2):229-247
19. Ali K, Raja M. COVID-19: dental aerosol contamination in open plan dental clinics and future implications. Evid Based Dent 2021; 22, 54–55. https://doi.org/10.1038/s41432-021-0168-0
20. Rhodus, EK, Bardach SH, Abner EL, Gibson A, Jicha GA. COVID-19 and geriatric clinical trials research. Aging clinical and experimental research 2020; 32(10), 2169–2172. https://doi.org/10.1007/s40520-020-01705-x
21. Laggis CW, Williams VL, Yang X, Kovarik CL: Research techniques made simple: teledermatology in clinical trials. J Invest Dermatol 2019; 139:1626–33. e1.
22. Eagleson R, Altamirano-Diaz L, McInnis A, Welisch E, De Jesus S, Prapavessis H, et al. Implementation of clinical research trials using web-based and mobile devices: challenges and solutions. BMC Med Res Methodol. 2017;17(1):43.
23. Padmavathi Vanka, T. Sudha. Big Data Technologies: A Case Study. Research J. Science and Tech. 2017; 9(4): 639-642.
24. Leo DencelinX, Ramkumar T. Distributed Machine Learning Algorithms to classify Protein secondary structures for Drug Design – A Survey. Research J. Pharm. and Tech. 2017; 10(9): 3173-3180.
25. Vimal Kumar Stephen. K, V. Mathivanan. Adjusting Healthcare Innovation and Software Necessities through design thinking. Research J. Pharm. and Tech 2017; 10(10):3537-3538.
26. P. Shanmuga Sundari, M. Subaji, J. Karthikeyan. A Survey on effective similarity Search Models and Techniques for Big data Processing in Healthcare System. Research J. Pharm. and Tech. 2017; 10(8): 2677-2684.
27. Pardeep Kumar Sharma, Amit Sachdeva, Cherry Bhargava. Fuzzy logic: A tool to predict the Renal diseases. Research Journal of Pharmacy and Technology. 2021; 14(5):2598-2.