HIT 760 Health Informatics Capstone: Revolutionising Dental Health Care

HIT 760
Health IT Capstone Paper
Revolutionizing Dental Health Care: Harnessing the Potential of Artificial Intelligence for Advanced Diagnosis and Treatment in the Modern Era of Dentistry By Sai Krishna Athota (VI54177)
HIT 760 Health Informatics Capstone
Office of Professional Programs
Abstract
To streamline their operations, numerous industries across the globe are turning to technological advancements, particularly artificial Intelligence. Employee efficiency and output are the two
primary targets of performance intelligence initiatives. Innumerable forms of artificial Intelligence are currently employed in healthcare delivery, and several corporations are interested in health sciences. More accurate diagnoses and vital treatment suggestions are possible thanks to several critical differences. These differences are essential to ensure the right management functions deal with and include the patient’s concerns. Except for certain hypothetical situations when AI completely replaces human workers, this would greatly slow down the automation of healthcare services. Therefore, we need to discuss the potential benefits of AI technology for healthcare and the ethical concerns governing AI usage.
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HIT 760 Health Informatics Capstone
Office of Professional Programs
Introduction
Statement of the Topic
AI has opened a new window of opportunity to revolutionize healthcare and improve patient outcomes (Almoammar, 2024). More precise and early diagnoses and highly individualized treatments suited to each patient’s genetic composition and ailment may be possible with the help of AI that analyses large amounts of health data in search of patterns too subtle or complicated for human study. Although artificial Intelligence is still in its infancy, it has the potential to revolutionize healthcare by bringing forth more accurate and predictive diagnoses and treatments. Artificial intelligence (AI) systems, such as deep neural networks, have outperformed humans in detecting cancer, neurological disorders, and diabetic retinopathy. One AI system, for instance, achieved a 90% success rate in diagnosing pediatric illnesses using only patient medical records. AI surpassed both general practitioners and paediatricians. An AI system attained a sensitivity of 95% when detecting diabetic retinopathy using eye scans, but the accuracy rate for ophthalmologists was just 80%. Artificial Intelligence helped lower the rate of false negatives, which averted avoidable blindness (Taher et al., 2023).
A Brief History of the Topic
Up until recently, the dental profession was hesitant to adopt AI. Radiography, electronic
recordkeeping, robotic assistance, caries detection, and dental imaging diagnostics are just a few
of the dental-related domains these technological advancements have influenced. Endodontics
has followed the trend of other branches of dentistry in recent years in investing more resources
into AI research. Endodontists should refresh their knowledge of AI. In light of this, this study
aims to synthesize previous research on the application of artificial Intelligence (AI) in
endodontics, clinical decision-making, and diagnostics and to identify any current limitations in
the use of AI in this area of dentistry (Mahesh Batra & Reche, 2023).
Alotaibi & Deligianni (2023) assert that almost everyone in the dental industry is quickly getting
ready to adapt to the new AI era in oral health care, as it can potentially improve early disease
detection and provide patients with more tools. This includes lawmakers, researchers, schools,
and practices. At the symposium, HSDM Dean William Giannobile spoke about how AI can
revolutionize oral healthcare by improving disease detection and treatment and expanding
access to quality healthcare for all. With the real buzz surrounding AI’s increasing impact on
dentistry, HSDM convened over 400 top dental professionals, academics, students, AI experts,
ethicists, and lawmakers from 30 nations. Participants could participate in the event’s seminars,
keynote speeches, and themed panel discussions in person or online. Over sixty-five research
projects from artificial Intelligence (AI) and dentistry were showcased at a poster session. These
studies comprised various device prototypes, patient-facing smartphone apps, and other ongoing
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HIT 760 Health Informatics Capstone
Office of Professional Programs
technologies. An esteemed panel of judges recognized multiple presentations. Academics are
embracing AI to enhance patient outcomes and address health issues affecting populations
(Sigala, 2021). For this to be successful, there must be a breakdown in the barriers between
different branches of science.
Location of Focus
To diagnose diabetic retinopathy and macular degeneration, AI systems can examine optical
coherence tomography (OCT) scans of the eyes. Compared to human ophthalmologists, AI has
shown to be just as good, if not better, in detecting and grading these problems. Artificial
Intelligence has demonstrated comparable capabilities in analyzing mammograms to detect
cancerous breast lesions and lung CTs to detect cancerous nodules. AI can increase access to
expert analysis by automating imaging test interpretation, allowing for the early detection of
problems. In addition to photos, AI can analyze genetic profiles, medical histories, lab tests, and
patients to discover diseases that have yet to be diagnosed. With hospital medical records, an AI
system created by DeepMind could anticipate numerous cases of acute kidney injury before
doctors could, allowing for early treatment. Machine learning algorithms can analyze blood test
results to foretell the start of liver disease and other serious health complications. Artificial
Intelligence can develop novel diagnostic biomarkers and disease risk factors by analyzing
correlations among billions of data points in medical records, medications, demographic
information, and family trees. Using natural language processing, AI systems can decipher
medical records and doctors’ notes. With this, human analysis is enhanced. For example, Saykara
developed an AI virtual scribe that can mechanically transcribe doctor-patient discussions and
input the relevant information into EHRs. Clinical documentation is now automated (Zhou, 2021).

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