From climate change to cavities, symposium highlights AI’s impact on oral health care.
Recognizing that artificial intelligence could lead to earlier diagnosis of oral diseases and put more tools in the hands of patients, most dental practices, dental schools, oral health researchers, and policymakers are rapidly positioning themselves to evolve with the dawning AI movement in oral health care.
Virtual reality technology – associative photo. Image credit: stem.T4L via Unsplash, free license
Experts shared these insights and more at the inaugural Global Symposium on AI and Dentistry, held Nov. 3-4 at the Harvard School of Dental Medicine (HSDM).
“AI promises to transform the way we practice oral health care, pinpoint and treat diseases and conditions, and increase equitable access to care and treatment,” HSDM Dean William Giannobile said at the symposium.
The tangible energy around AI’s growing influence on dentistry prompted HSDM to gather more than 400 leading dental practitioners, researchers, students, AI scientists, ethicists, and policymakers from 30 countries. Attendees joined event workshops, keynotes, and thematic panel discussions both in person and virtually.
A poster session included more than 65 research projects featuring a range of device prototypes, patient-facing smartphone apps, and other technologies under development at the intersection of AI and dentistry. A panel of judges honored several presenters.
Harnessing AI to address barriers in oral health care
For more than 40 years, researchers have been experimenting with ways to apply AI to dentistry, said Florian Hillen, founder and CEO of VideaHealth, a dental imaging startup launched from AI research conducted at Harvard and MIT.
Within the last decade, he said, AI capabilities have finally reached critical mass.
“AI-powered tools are now helping dentists identify dental decay in patients up to five years earlier,” he said. “The tech revolution is happening.”
Beyond opportunities to improve outcomes for individual patients, researchers are seizing AI to help solve population-level health challenges. To do so effectively, academia and industry will have to dissolve boundaries between scientific discipines, said keynote speaker Dimitrias Bertsimas of MIT.
At Harvard, cross-disciplinary teams are leveraging machine learning to identify patients whose social determinants of health put them more directly in the path of climate change-related impacts and a bevy of other risks to oral health.
“Are exposures to wildfires impacting oral health? If they become more frequent, who’s most vulnerable and how do we act on this information?” asked Francesca Dominici, director of the Harvard Data Science Initiative at the Harvard T.H. Chan School of Public Health.
She and a team of researchers are using AI to analyze satellite data, atmospheric chemistry models, and other factors, revealing which communities are most affected by increasingly frequent wildfires, extreme heat waves, and destructive storms.
Reduced air quality from fires and higher temperatures from warming climate can cause mouths to be drier, making people more prone to oral disease and tooth decay. Increased psychological stress from extreme weather events can increase risk for teeth grinding and temporomandibular joint (TMJ) disorders.
What’s more, natural disasters can disrupt access to dental facilities and care, Dominici added.
Augmenting, not replacing, human knowledge
Biomedical researchers are also deploying AI to speed up and optimize experiments, therapeutic discovery, and pre-clinical validation.
“[AI is] generating, acquiring, harmonizing, and refining data, and it can generate hypotheses, as well as simulate experiments and downstream outcomes,” said Marinka Zitnik, assistant professor of biomedical informatics in the Blavatnik Institute at Harvard Medical School.
It will revolutionize the way therapies are matched individually to patients, she said, and help design entirely new drugs and therapeutics.
A survey of 1,600 biomedical researchers revealed that 25 percent of them feel AI will be essential to their studies within the decade, she added.
Zitnik specializes in building knowledge graph AI models, which help contextualize and capture relationships within diverse sets of biomedical data. Her team has developed a knowledge graph AI model called TxGNN that describes 17,000 diseases using all available clinical and biomedical data. Once trained, it could predict how well any given therapeutic might treat a patient’s unique disease and recommend new uses for FDA-approved medications.
Source: HMS