Evidence map›Paper›PMID 40553330›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2025

Integrating Genetic Insights and Artificial Intelligence for Enhanced Oral and Maxillofacial Cancer Care.

A K M Shafiul Kadir, Md Asaduzzaman, Joye Kundu, Md Abdur Rahman, Md Golam Rabbany, Mohammad Ullah Shemanto, Rukaiya Akhter, Esrat Jahan Zerin, Amit Biswas

Abstract readReview
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In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

A K M Shafiul KadirQuest Bangladesh Biomedical Research Center, Dhaka, Bangladesh.
Md AsaduzzamanRangpur Community Dental College, Rangpur, Bangladesh.
Joye KunduDepartment of Biomedical Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh.
Md Abdur RahmanDental Unit, Rangpur Medical College, Rangpur, Bangladesh.
Md Golam RabbanyRangpur Community Dental College, Rangpur, Bangladesh.
Mohammad Ullah ShemantoAhsania Mission Cancer and General Hospital (AMCGH), Dhaka, Bangladesh.
Rukaiya AkhterAhsania Mission Cancer and General Hospital (AMCGH), Dhaka, Bangladesh.
Esrat Jahan ZerinUpazila Health Complex, Kalkini, Madaripur, Bangladesh.
Amit BiswasBangladesh College of Physicians & Surgeons (BCPS), Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) with genetic insights presents promising avenues for revolutionizing the landscape of oral and maxillofacial cancer (OMC) care. This proposed chapter, titled, aims to explore the synergistic potential of AI and genetic understanding in advancing OMC diagnosis, treatment, and prognosis. The chapter will delve into the genetic changes, including mutations and polymorphisms, contributing to the pathogenesis of oral squamous cell carcinomas (OSCCs) and other malignancies in the oral and maxillofacial region. Additionally, it will elucidate how AI-driven applications, such as computer vision and machine learning, offer novel opportunities for early detection and accurate diagnosis of OMC, thereby improving patient outcomes. By analyzing genetic data and medical records, AI algorithms can identify subtle patterns indicative of OMC development, enabling clinicians to intervene at earlier stages. Moreover, AI facilitates personalized treatment planning by integrating genetic markers with clinical data to tailor therapies based on individual patient profiles, maximizing efficacy and minimizing adverse effects. Furthermore, the chapter will discuss the role of AI in predicting treatment responses and long-term prognoses, aiding clinicians in making informed decisions and improving patient outcomes. Overall, this chapter aims to highlight the transformative potential of integrating genetic insights with AI technologies in OMC care. By leveraging AI-driven approaches for early detection, accurate diagnosis, and personalized treatment, we can significantly enhance patient outcomes and contribute to the broader understanding of AI's role in biomedical research and clinical practice.

Indexed as

Artificial IntelligenceMouth NeoplasmsBiomarkers, TumorHumansMachine LearningPrecision MedicinePrognosisBiomarkers, TumorArtificial intelligenceEarly detectionGenetic engineeringOral and maxillofacial cancerPersonalized treatment

Identifiers

PMID40553330

What OpenQuestion holds

Textmetadata
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.