SynthesisAsian Pacific journal of cancer prevention : APJCP2024
Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.
Synthesis in Asian Pacific journal of cancer prevention : APJCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 5 of them syntheses that pooled 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.
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.
Who cites it
10 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.Clinical oral investigations · 2026Pooled it
- Diagnostic Efficacy of Serum Biomarkers for Oral Cancer: An Updated Systematic Review and Meta-Analysis.Asian Pacific journal of cancer prevention : APJCP · 2026Pooled it
- Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Diagnostic performance of artificial intelligence in oral squamous cell carcinoma detection: a higher-order evidence synthesis through umbrella review and meta-meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review.Oral diseases · 2025Pooled it
- AI-Assisted Early Clinical Diagnosis of External Auditory Canal Squamous Cell Carcinoma: A Rare Case in a Young Male From Pakistan.Clinical case reports · 2026Article
- Review
- Non-Invasive Techniques for Early Detection of Oral Squamous Cell Carcinoma: A Narrative Review.Cancer management and research · 2026Review
- Differentiation of benign and malignant oral lesions through surface texture analysis and SVM modeling.Clinical oral investigations · 2025Article
- ANAPC10 expression predicts poor prognosis in oral squamous cell carcinoma and promotes tumor growth via the PI3K/AKT/mTOR pathway.Discover oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo summarize and compare the existing evidence on diagnostic accuracy of artificial intelligence (AI) models in detecting early oral squamous cell carcinoma (OSCC).
methodReview was performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Diagnostic Test Accuracy (PRISMA- DTA) checklist and the review protocol is registered under PROSPERO(CRD42023456355). PubMed, Google Scholar, EBSCOhost were searched from January 2000 to November 2023 to identify the diagnostic potential of AI based tools and models. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on QUADAS (Quality assessment of diagnostic accuracy studies)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 RevMan using a bivariate model parameter for the sensitivity and specificity and summary points, summary receiver operating curve (SROC), diagnostic odds ratio (DOR) confidence region, and area under curve (AUC) were calculated.
resultsFourteen studies were included for qualitative synthesis and for meta-analysis. Included studies had presence of low to moderate risk of bias. Pooled sensitivity and specificity of 0.43 (CI 0.18- 0.71) and 0.50 (CI 0.20- 0.80) was observed with a pooled positive likelihood ratio of (PLR) 0.86 (0.43 - 1.71) and negative likelihood ratio (NLR) of 1.04 (0.42 - 1.68) was observed with DOR of 0.78 (0.12 - 5.18) and overall accuracy (AUC) being 0.45 respectively.
conclusionAI based tools has poor to moderate overall diagnostic accuracy. However, to validate our study findings further more standardized diagnostic accuracy studies should be conducted with proper reporting through QUADAS-2 tool. Thus, we can conclude AI based based tool for secondary level of prevention for early OSCC under early diagnosis and prompt treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.