Evidence map›Paper›PMID 38263027›Full record

SynthesisBMC oral health2024

Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.

Nishath Sayed Abdul, Ganiga Channaiah Shivakumar, Sunila Bukanakere Sangappa, Marco Di Blasio, Salvatore Crimi, Marco Cicciù, Giuseppe Minervini

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  9. Navigating the ethical and legal landscape of artificial intelligence in dentistry.Journal of advanced periodontology & implant dentistry · 2026
    Article
  10. Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nishath Sayed AbdulDepartment of OMFS & Diagnostic Sciences, College of Dentistry, Riyadh Elm, University, Riyadh, Saudi Arabia.
Ganiga Channaiah ShivakumarDepartment of Oral Medicine and Radiology, People's College of Dental Sciences and Research Centre, People's University, Bhopal, 462037, India. shiva21375@gmail.com.
Sunila Bukanakere SangappaDepartment of Prosthodontics and Crown & Bridge, JSS Dental College and Hospital, JSS Academy of Higher Education and Research, Mysuru, Karnataka, India.
Marco Di BlasioDepartment of Medicine and Surgery, University Center of Dentistry, University of Parma, 43126, Parma, Italy. marco.diblasio@studenti.unipr.it.
Salvatore CrimiDepartment of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy.
Marco CicciùDepartment of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy.
Giuseppe MinerviniSaveetha Dental College & Hospitals, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai, India. Giuseppe.minervini@unicampania.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince AI algorithms can analyze patient data, medical records, and imaging results to suggest treatment plans and predict outcomes, they have the potential to support pathologists and clinicians in the diagnosis and treatment of oral and maxillofacial pathologies, just like every other area of life in which it is being used. The goal of the current study was to examine all of the trends being investigated in the area of oral and maxillofacial pathology where AI has been possibly involved in helping practitioners.

methodsWe started by defining the important terms in our investigation's subject matter. Following that, relevant databases like PubMed, Scopus, and Web of Science were searched using keywords and synonyms for each concept, such as "machine learning," "diagnosis," "treatment planning," "image analysis," "predictive modelling," and "patient monitoring." For more papers and sources, Google Scholar was also used.

resultsThe majority of the 9 studies that were chosen were on how AI can be utilized to diagnose malignant tumors of the oral cavity. AI was especially helpful in creating prediction models that aided pathologists and clinicians in foreseeing the development of oral and maxillofacial pathology in specific patients. Additionally, predictive models accurately identified patients who have a high risk of developing oral cancer as well as the likelihood of the disease returning after treatment.

conclusionsIn the field of oral and maxillofacial pathology, AI has the potential to enhance diagnostic precision, personalize care, and ultimately improve patient outcomes. The development and application of AI in healthcare, however, necessitates careful consideration of ethical, legal, and regulatory challenges. Additionally, because AI is still a relatively new technology, caution must be taken when applying it to this industry.

Indexed as

AlgorithmsArtificial IntelligenceFaceHumansImage Processing, Computer-AssistedMedical RecordsMouthArtificial intelligenceDiagnosisImage analysisMachine learningOral and maxillofacial pathologyPredictive modelling

Identifiers

PMID38263027
PMCPMC10804575

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.