Evidence map›Paper›PMID 40735535›Full record

ArticleJournal of oral biology and craniofacial research

Strength, weakness, opportunities and challenges (SWOC) experience of histopathology image analysis, enhanced by artificial intelligence.

Narendra Nath Singh, Ankita Tandon, Pavithra Jayasankar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of oral biology and craniofacial research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
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

3 authors.

Narendra Nath SinghDepartment of Oral Pathology, Microbiology, and Forensic Odontology, Dental Institute, RIMS, Ranchi, India.
Ankita TandonDepartment of Oral Pathology, Microbiology, and Forensic Odontology, Dental Institute, RIMS, Ranchi, India.
Pavithra JayasankarDepartment of Oral Pathology, Microbiology, and Forensic Odontology, Dental Institute, RIMS, Ranchi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is reshaping the landscape of oral cancer diagnosis through the analysis of digital imaging. By promoting early detection, enhancing diagnostic precision, and enabling personalised treatment approaches, AI holds the potential to significantly improve patient outcomes. However, it is important to carefully consider concerns related to bias, costs, data quality, and regulatory standards. Histopathology image analysis is critical for precise and early diagnosis, particularly cancer detection. It improves consistency, decreases subjectivity, and enables accurate assessment. Its combination with AI allows for faster diagnostics, remote consultations, sophisticated research, and personalised treatment methods, making it an essential tool in modern pathology and healthcare. To fully realise its promise in improving patient care and diagnostics for oral cancer, strategic investments, multidisciplinary cooperation, and strong regulatory frameworks are essential. This narrative review highlights the potential and challenges that lie ahead while advocating for a balanced approach that combines technical innovation with ethical and regulatory vigilance based on a comprehensive literature search and our team's personal experience.

Indexed as

Artificial intelligenceDigital pathologyHistopathologyImage analysisImage-based diagnostics

Identifiers

PMID40735535
PMCPMC12305316

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.