Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

Importance of early diagnosis and surgical treatment of calcified amorphous tumor-related native valve endocarditis caused by Escherichia coli: a case report.

BACKGROUND: Unlike Escherichia coli bacteremia, which is common, E. coli endocarditis is uncommon, particularly in patients with native valve, leading to its delayed diagnosis. CASE PRESENTATION: We present a case of infective endocarditis caused by E. coli in a 78-year-old Japanese man with type 2 diabetes, involving persistent bacteremia and vegetation on the mitral valve (measuring 18 × 4.2 mm in diameter). He presented with recurrent fever after antimicrobial treatment for pyelonephritis. He received antibiotic therapy for 6 weeks and required surgical removal of a calcified amorphous tumor and vegetation with mitral valvuloplasty 7 days after admission. Despite an episode of multiple cerebral infarctions, he recovered fully from the infection. CONCLUSIONS: Follow-up blood cultures should be performed for Gram-negative bacilli bacteremia among patients with unknown focus and an atypical clinical course after treatment. Early diagnosis and aggressive surgical intervention are paramount to achieving good clinical outcomes.