In this special guest feature, Florian Hillen, founder and CEO, VideaHealth, points out that Like many other industries within the healthcare ecosystem, dentistry is beginning to adopt artificial intelligence (AI) solutions to improve patient care, lower costs, and streamline workflows and care delivery.
Like many other industries within the healthcare ecosystem, dentistry is beginning to adopt artificial intelligence (AI) solutions to improve patient care, lower costs, and streamline workflows and care delivery. While the dental profession is no stranger to cutting-edge technology, AI represents such a revolutionary change that few organizations have the knowledge and skill sets to implement an effective strategy.
This is particularly important when applying AI to diagnose and treat patients. Ideally, AI should exceed human-level performance in speed, efficiency and accuracy. But unlike traditional technologies that are simply powered up and put to work, AI must be trained, conditioned and trusted to perform as expected even under difficult or unusual circumstances.
This requires dental providers to implement an AI training engine – what we call an AI factory – that incorporates key elements in the creation and conditioning of AI models. These include things like the data pipeline, labeling operations and software infrastructure, as well as the machine learning programs themselves, all of which are designed to detect a wide range of dental pathologies and provide highly tailored courses of treatment based on patients’ needs.
Turning Data into Knowledge
Training AI models is no easy job. It requires enormous amounts of data and strict guidance as to how that data is presented so as not to bias the algorithm, which can skew results and lead to health inequities. With the ability to support immense computing power to process calculations very quickly, coupled with access to aggregated and centralized data stores, today’s platforms can comb through hundreds of millions of data points from service providers, insurance companies, universities and other sources to ensure that results are not just accurate but impartial as well.
This is what gives AI-driven processes the ability to enhance the clinical experience. By eliminating human error and bias, AI delivers more accurate diagnoses, better treatment options and fewer mistakes that must be corrected, usually at great expense or pain, at a later date.
It is important to note the data used to inform these models is not merely textual or numeric in nature, but pictographic as well. AI scans X-rays, MRIs and other visual elements to detect decay, abscesses and even cancers, sometimes long before they become apparent to the naked eye. This technology can also be used to customize crowns, bridges and implants much more quickly and more accurately than traditional procedures.
A key problem in the dental industry is the fractured nature of most practices. The vast majority of dental practices are independently owned and operated, which makes data collection and analysis difficult at best, particularly at the scale needed to draw accurate conclusions. While this has started to change in recent years with the rise of dental service organizations (DSOs) and increased consolidation within the insurance industry, to date there has been very little progress in capturing broad data sets, which are largely subject to data privacy and protection laws.
The Factory Approach
New companies are looking to change this with the development of factory-style data preparation modeled on the analytics engines of Netflix and other data-driven organizations. Using highly automated processes that can be quickly scaled to accommodate massive data sets from a multitude of sources, a properly designed AI factory can streamline the analytics process to ensure high-quality data is being fed into AI models.
This, in turn, produces high-quality results – much the same way that automation has improved the manufacturing of cars, food and other physical products.
Perhaps one of the most basic improvements this factory approach to AI has achieved is cavity detection. A recent FDA trial demonstrated how AI-driven software trained on a factory model can reduce the number of missed cavities by 43% and cut the number of false positives by 15%. All dentists involved in the trial, regardless of training and experience, reported a distinct improvement in the ability to make accurate diagnoses.
Dentistry is a highly specialized sector of the broader healthcare industry, and as such it relies on unique data points in order to provide effective service to patients. At the same time differences in experience levels, equipment and diagnostic capabilities vary greatly, so much so that in most cases ten different dentists will provide ten different diagnoses.
By bringing order to this environment, an AI factory not only streamlines dental care and reduces costs but greatly increases accuracy in both the assessment and treatment of patients. Professional discrepancies will remain, of course, but disagreements on data and how it should be treated will be less. The end result should be better health outcomes and less burden, financial and otherwise, on today’s bloated, largely redundant healthcare system.
The knowledge to accomplish this feat is already out there. All that is needed is an efficient, effective means of utilizing it.
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