Effect of AI-powered patient management systems in dental clinics.

S Mohammed Harris, Revanth S Soonthodu, Saloni Bharti, Maneesha Das, Christina Sarah James, K Aadisha

Abstract read

In one paragraph

Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

S Mohammed HarrisDepartment of Periodontics, SRM Dental College, Bharathi Salai, Ramapuram, Chennai-89, Tamil Nadu, India.

Revanth S SoonthoduDepartment of Orthodontics, KVG Dental College and Hospital, Kurunjibhag, Sullia, Karnataka 574327, India.

Saloni BhartiDepartment of Dentistry, ESIC Medical College and Hospital, Kateshar, Bihta, Bihar 801103, India.

Maneesha DasDepartment of Conservative Dentistry and Endodontics, Institute of Dental Sciences, Siksha 'O' Anusandhan University, Bhubaneswar-751003, Odisha, India.

Christina Sarah JamesDepartment of Oral Pathology, Travancore Dental College, Thattamala, Mylapure, Palathara, Kollam, Thazhuthala, Kerala 691020, India.

K AadishaDepartment of Public Health, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University) No. 1, Ramachandra Nagar, Porur, Chennai - 600116, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dental clinics frequently experience operational inefficiencies due to fragmented data systems and manual administrative processes. Therefore, it is of interest to evaluate the impact of AI-powered patient management systems on workflow efficiency, data accuracy and patient satisfaction across five clinics. Appointment scheduling time reduced from 9.6 ± 2.4 to 3.2 ± 1.1 minutes, billing errors declined from 12.5 ± 3.8% to 3.7 ± 1.9% and data entry accuracy improved to 96.2 ± 2.7%. Patient satisfaction scores increased across communication, access to records, waiting time experience and overall service quality. Thus, AI integration was associated with measurable improvements in administrative precision and clinical workflow performance.

Indexed as

Artificial intelligence (AI)clinical decision supportdental clinicshealthcare efficiencypatient management system

Identifiers

PMID42282411

DOI10.6026/973206300222045

PMCPMC13252290

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.