{Reference Type}: Journal Article
{Title}: Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?
{Author}: Marshall C;Forbes J;Seidman MD;Roldan L;Atkins J;
{Journal}: Otol Neurotol
{Volume}: 45
{Issue}: 8
{Year}: 2024 Sep 1
{Factor}: 2.619
{DOI}: 10.1097/MAO.0000000000004267
{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.
METHODS: Prospective cohort study.
METHODS: Tertiary care center.
METHODS: 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.
METHODS: Summaries of the patient's symptoms were supplied to three publicly available AI platforms: Chat GPT 4.0, Google Bard, and WebMD "Symptom Checker."
METHODS: 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.