Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?

ABSTRACT

OBJECTIVE: Investigate the precision of language-model artificial intelligence (AI) in diagnosing conditions by contrasting its predictions with diagnoses made by board-certified otologic/neurotologic surgeons using patient-described symptoms. STUDY DESIGN: Prospective cohort study. SETTING: Tertiary care center. PATIENTS One hundred adults participated in the study. These included new patients or established patients returning with new symptoms. Individuals were excluded if they could not provide a written description of their symptoms. INTERVENTIONS: Summaries of the patient's symptoms were supplied to three publicly available AI platforms Chat GPT 4.0, Google Bard, and WebMD "Symptom Checker." MAIN OUTCOME MEASURES: This study evaluates the accuracy of three distinct AI platforms in diagnosing otologic conditions by comparing AI results with the diagnosis determined by a neurotologist with the same information provided to the AI platforms and again after a complete history and physical examination. RESULTS: The study includes 100 patients (52 men and 48 women; average age of 59.2 yr). Fleiss' kappa between AI and the physician is -0.103 (p < 0.01). The chi-squared test between AI and the physician is χ2 = 12.95 (df = 2; p < 0.001). Fleiss' kappa between AI models is 0.409. Diagnostic accuracies are 22.45, 12.24, and 5.10% for ChatGPT 4.0, Google Bard, and WebMD, respectively. CONCLUSIONS: Contemporary language-model AI platforms can generate extensive differential diagnoses with limited data input. However, doctors can refine these diagnoses through focused history-taking, physical examinations, and clinical experience-skills that current AI platforms lack.