evidencegaps
Primary study · included by 1 review

Application of artificial intelligence and machine learning for prediction of oral cancer risk

Alhazmi 2021 · Alhazmi Anwar, Alhazmi Yaser, Makrami Ali, et al. · Journal of Oral Pathology & Medicine · 2021

Included by

Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.
Non-Cochrane · DTA;prognostic · 2026 · cited as Alhazmi 2021 · Table 1; Figures 3-5
Outcome this study informsPooled effectEvidenceCertainty
Diagnostic accuracy
AI models for oral cancer diagnosis or risk assessment (image-based and text-based) vs Reference standard (histopathology where available, otherwise expert clinical diagnosis)
Pooled accuracy (proportion) 0.89 [0.83, 0.95]11 studiesGRADE not reported
Diagnostic sensitivity
AI models for oral cancer diagnosis or risk assessment (image-based and text-based) vs Reference standard (histopathology where available, otherwise expert clinical diagnosis)
Pooled sensitivity (proportion) 0.90 [0.81, 0.98]13 studiesGRADE not reported
Diagnostic specificity
AI models for oral cancer diagnosis or risk assessment (image-based and text-based) vs Reference standard (histopathology where available, otherwise expert clinical diagnosis)
Pooled specificity (proportion) 0.89 [0.84, 0.95]14 studiesGRADE not reported

1 including review in total.