Deducing differential diagnoses in movement disorders: Neurology residents versus a novel mobile medical application (Neurology Dx)

Abstract

AIM: The aim of this study was to detect the diagnostic accuracy of a novel app (Neurology Dx) vis-à-vis neurology residents. METHODS: A multicenter cross-sectional study involving seven leading teaching neurology institutes in India was conducted by recruiting 100 neurology residents. Primary outcome was proportion of correctly identified high likely gold standard differential diagnoses. Secondary outcomes were proportions of correctly identified first high likely, first three high likely, first five high likely, and combined moderate plus high likely gold standard differentials. RESULTS: Four sets comprising 15 movement disorder vignettes each (total 60) were tested on 100 neurology residents (one set for each resident) and also on the app (60 vignettes). Residents correctly identified the gold standard “high likely” differentials with a frequency of 13.6% as against 41.5% by the app (95% confidence interval [CI]: 21.9–34.1). On combining “high” and “moderate likely” differentials, residents could accurately identify gold standard differentials with a frequency of 10.8% as against 37.9% by the app (95% CI: 22.6–31.9). The residents correctly identified first five high likely gold standard differentials with a frequency of 13.5% versus 23.7% by the app (95% CI: 5.3–15.9). The residents correctly identified first three high likely gold standard differentials with a frequency of 13.0% versus 15.8% by the app (95% CI: -1.2–7.9). Residents correctly identified the first “high likely” gold standard differential in 32.3% as against 35% by the app (95% CI: -8.4–15.6). CONCLUSIONS AND RELEVANCE: This study suggests that an app (Neurology Dx) is capable of generating differential diagnoses to complement clinical reasoning of neurology residents (CTRI/2017/06/008838).

Keywords

• artificial intelligence
• medical education
• movement disorders
• resident training