%0 Journal Article
%T Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?
%A Marshall C
%A Forbes J
%A Seidman MD
%A Roldan L
%A Atkins J
%J Otol Neurotol
%V 45
%N 8
%D 2024 Sep 1
%M 39142308
%F 2.619
%R 10.1097/MAO.0000000000004267
%X <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>Prospective cohort study.<BR><B>METHODS: </B>Tertiary care center.<BR><B>METHODS: </B>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.<BR><B>METHODS: </B>Summaries of the patient's symptoms were supplied to three publicly available AI platforms: Chat GPT 4.0, Google Bard, and WebMD "Symptom Checker."<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.