The Use of Artificial Intelligence in Gerodontology in the Age of Digital Technology

Introduction

With advancements in digital healthcare technologies like artificial intelligence (AI), 3D printing, robotics, nanotechnology, etc., healthcare is taking shape right before our eyes. Healthcare digitization offers a variety of opportunities for lowering human error rates, enhancing clinical results, monitoring data over time, etc. A number of health-related domains, including developing new clinical systems, maintaining patient information and records, and treating various illnesses, depend heavily on AI techniques ranging from machine learning to deep learning (Ullah et al., 2020).

Combining telecommunications and dentistry, teledentistry entails the transmission of clinical data and images over vast distances for dental consultation and treatment planning. Teledentistry has the potential to increase accessibility, enhance oral healthcare delivery, and cut costs. Additionally, it could end the disparities in oral healthcare between rural and urban areas (Jampani et al., 2011).

Academic medical centres, community hospitals, managed-care organisations, rural hospitals, and other settings all use telemedicine today. It is also used internationally to connect healthcare providers in developing nations with hospitals in developed nations. Remote access to medical care now has previously unheard-of opportunities thanks to developments in digital communication, telecommunication, and the Internet (Dils et al., 2004).

In recent years, there have been numerous technological advancements in the dental industry. Digital diagnostic imaging services, computers, telecommunications technology, devices, and software for analysis and follow-up and even artificial intelligence and machine learning (Clark, 2023). The term “artificial intelligence” (AI) is a general one that refers to the use of a computer to simulate intelligent behaviour with the least amount of human involvement (Hamet & Tremblay, 2017). It is a subfield of computer science that can analyse intricate medical data. Their ability to find significant relationships in a data set can be used in many clinical situations for diagnosis, treatment, and outcome prediction (Ramesh et al., 2004).

On the other hand, the subset of artificial intelligence known as machine learning (ML) uses data to enable computers to “learn” and thus enhance performance (Mitchell, 1997). Without being explicitly programmed to do so, machine learning algorithms create a model from sample data, also referred to as training data, in order to make predictions or decisions (Sudweeks & Gero, 2012).

A surge in interest in AI occurred in the 1980s and 1990s. Different clinical settings in the field of medicine have made use of artificial intelligence techniques like fuzzy expert systems, Bayesian networks, artificial neural networks, and hybrid intelligent systems (Amisha et al., 2019). In 2016, compared to other industries, healthcare applications received the largest share of investments in AI research (CB Insights Research, 2017).

The two subtypes of artificial intelligence (AI) in medicine are virtual and physical (Hamet & Tremblay, 2017). Applications like electronic health record systems and neural network-based treatment decision-making are examples of the virtual part. The physical section focuses on elderly care, robotic surgery assistants, and intelligent prosthetics for the disabled. The foundation of evidence-based medicine is the development of associations and patterns from the already-existing database of data in order to establish clinical correlations and insights. In the past, statistical techniques were used to identify these patterns and associations. Using flowcharts and database approaches, computers can learn how to diagnose patients (Amisha et al., 2019).

The diagnosis of various diseases can also be made most effectively using AI techniques. The use of artificial intelligence (AI) in medical services offers previously unheard-of opportunities to recover patient and clinical group results, cut costs, etc. The models used do not just involve computers; they also involve giving patients, “family,” and medical service experts access to data and the ability to make suggestions, as well as disclosing data to facilitate joint evaluation building (Musleh et al., 2019)