Cervical cancer is the second most common cancer in women, and the role of human papillomavirus (HPV) testing in its etiology is becoming increasingly important. We aimed to evaluate the performance of an artificial intelligence (AI)-assisted colposcopy system in detecting high-risk human papillomavirus (HR-HPV) subtypes in cervical intraepithelial neoplasia (CIN) 2 patients. We conducted a post-hoc analysis of a previous observational study that developed an AI algorithm for colposcopic images of patients with CIN2. Out of 78 patients with HR-HPVs (HPV 16, 18, 31, 33, 35, 45, 52 and 58), 60 (76.9%) had positive AI colposcopy results. The accuracy, sensitivity and specificity of the AI-assisted colposcopy system in detecting HR-HPVs (16, 18, 31, 33, 35, 45, 52 and 58) were 0.689, 0.769 and 0.537, respectively. This study is the first to focus on using AI to detect high-risk subtypes. The AI algorithm accurately detects the characteristics of HR-HPVs. It can be used to detect high-risk CIN types in patients with cervical dysplasia based only on colposcopic imaging findings and is potentially a valuable tool for follow-up.