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DENTAL TECHNOLOGY, APRIL-JUNE 2023
exclusive interview 29

Generative A.I refers to A.I algorithms (A.I
algorithms typically used for unsupervised
learning) that can generate text, audio, and
images. These algorithms require a huge
amount of data from which they can learn sta-
tistical properties and then can generate con-
vincing text, images, etc. Services such as the
now popular ChatGPT use Generative algo-
rithms to first learn from past data, and using
an initial seed, they can generate data of dif-
ferent modalities. Generally speaking, these
services utilize an algorithm or a neural net-
work architecture known as transformers
(Generative Pretrained Transformers, GPT to
be precise). Utilizing terabytes of data from
different sources these networks cost millions
of dollars to train using high-performance
GPUs and utilizing techniques such as
Reinforcement Learning from Human
Feedback.
I see Generative A.I as the kind of technol-
ogy that can revolutionize the dental industry
or in a broader sense, the medical industry.
From a dentist’s perspective, we can use gen-
erative A.I to aid in the generation of synthet-
ic data (think dental/oral diagnosis datasets)
that algorithms can utilize to build better Data Science and A.I algorithms process huge amounts
detection networks. Another area where I of machine data to predict/recommend future solutions
think Generative A.I can really change the
game is by automating the design process. We in an effort to achieve the 3P principle. The first P stands
can use Generative A.I to generate Dental for Prognosis i.e identifying the root cause via data. The
scans just by providing an appropriate
prompt. second P stands for Prevent, using past data to prevent
future errors. The final P stands for Predict, using past
What is the concept of Predictive
Q.Maintenance? data to predict future errors.
Predictive Maintenance refers to utilizing data
analytics to determine the condition of an
equipment in order to estimate when mainte-
nance is required. Predictive Maintenance typ-
ically utilizes predictive analytics or data sci-
ence and A.I algorithms to be able to predict
failure in the equipment. Common data
modalities include sensor values, text log
files, etc. Predictive Maintenance in the realm
of the dental industry is extremely valuable as
it allows users to quickly and effectively man-
age their machines and achieve higher mill
output.
Data Science and A.I algorithms process
huge amounts of machine data to predict/rec-
ommend future solutions in an effort to
achieve the 3P principle. The first P stands for
Prognosis i.e identifying the root cause via
data. The second P stands for Prevent, using
past data to prevent future errors. The final P
stands for Predict, using past data to predict
future errors. such algorithms is an incredibly complex task. Dental labs and clinics
should consider what problems to solve using data and should have a
What must dental labs and dental clinics consider when thorough analysis on what provides them with the most value.
Q.integrating AI technologies into their daily workflows? 3. Model Deployment and Metric analysis: Quantifiable met-
To be able to iterate A.I technologies in their daily workflow, the rics should be kept in place so as to measure the performance of an A.I
following are the most important ingredients of a successful A.I/data system including both model specific and business-specific metrics.
product: 4. Feedback system and iterate: Models or any A.I system
1. Data Strategy: Building models is the easy part but collecting should have a robust feedback system in place that can notify the devel-
good quality and relevant data is the most important aspect of any data oper/stakeholders of how it is affecting the organization. Rapid iteration
science/A.I project and experimentation is also an essential part of integrating A.I technolo-
2. Business Problem Formulation: Identifying where to apply gies into their daily workflows.

Aadit Kapoor is a passionate full stack data scientist specializing in building end to end data driven applications with extensive experience leading million-dollar projects
from scratch in various industries, including healthcare/medicine, logistics etc. Additionally he is spearheading the effort to build A.I applications in predictive maintenance at DGSHAPE,
and his work was featured in the prestigious PyData Global 2022 Conference. He is based in San Francisco.