Pneumoparotid is a rare condition of parotid swelling. The presence of the air in gland parenchyma is caused by an incompetent Stensen’s duct with high pressure may cause the acini’s rupture. We reviewed 49 manuscripts, from 1987 to today, that enrolled a total of 54 patients with pneumoparotid. Our review evaluated the following evaluation parameters: gender, age, etiology, clinical presentation, treatment, days of resolution after diagnosis, relapse and complications. The most frequent etiology is self-induction by swelling the cheeks (53.7%). This cause mainly involves children (74%), for conflicts with parents, excuses for not going to school, nervous tics or adults (16%) with psychiatric disorders. Iatrogenic causes are also frequent (16.6%), for dental treatments (55.5%) or use of continuous positive airway pressure (CPAP) (33.4%). Medical therapy is the most practiced (53.7%), in most cases it is combined with behavioral therapy (25.9%) or psychotherapy (25.9%). Surgery is rarely used (9.2%) as a definitive solution through parotidectomy (50%) or ligation of the duct (50%). The most common complication is subcutaneous emphysema (24.1%), sometimes associated with pneumomediastinum (5.5%). Careful treatment and management are necessary to ensure the resolution of the pathology and counteract the onset of complications.

Background/Objectives: Artificial Intelligence (AI) is increasingly being applied in otolaryngology, including cochlear implants (CIs). This study evaluates the accuracy and completeness of ChatGPT-4 and Microsoft Copilot in determining the appropriate implantation side based on audiological and radiological data, as well as the presence of tinnitus. Methods: Data from 22 CI patients (11 males, 11 females; 12 right-sided, 10 left-sided implants) were used to query both AI models. Each patient’s audiometric thresholds, hearing aid benefit, tinnitus presence, and radiological findings were provided. The AI-generated responses were compared to the clinician-chosen sides. Accuracy and completeness were scored by two independent reviewers. Results: ChatGPT had a 50% concordance rate for right-side implantation and a 70% concordance rate for left-side implantation, while Microsoft Copilot achieved 75% and 90%, respectively. Chi-square tests showed significant associations between AI-suggested and clinician-chosen sides for both AI (p < 0.05). ChatGPT outperformed Microsoft Copilot in identifying radiological alterations (60% vs. 40%) and tinnitus presence (77.8% vs. 66.7%). Cronbach’s alpha was >0.70 only for ChatGPT accuracy, indicating better agreement between reviewers. Conclusions: Both AI models showed significant alignment with clinician decisions. Microsoft Copilot was more accurate in implantation side selection, while ChatGPT better recognized radiological alterations and tinnitus. These results highlight AI’s potential as a clinical decision support tool in CI candidacy, although further research is needed to refine its application in complex cases.