Explainable AI decision support improves accuracy during telehealth strep throat screening

Abstract

Artificial intelligence-based (AI) clinical decision support systems (CDSS) using unconventional data, like smartphone-acquired images, promise transformational opportunities for telehealth; including remote diagnosis. Although such solutions’ potential remains largely untapped, providers’ trust and understanding are vital for effective adoption. This study examines how different human–AI interaction paradigms affect clinicians’ responses to an emerging AI CDSS for streptococcal pharyngitis (strep throat) detection from smartphone throat images. In a randomized experiment, we tested explainable AI strategies using three AI-based CDSS prototypes for strep throat prediction. Participants received clinical vignettes via an online survey to predict the disease state and offer clinical recommendations. The first set included a validated CDSS prediction (Modified Centor Score) and the second introduced an explainable AI prototype randomly. We used linear models to assess explainable AI’s effect on clinicians’ accuracy, confirmatory testing rates, and perceived trust and understanding of the CDSS. The study, involving 121 telehealth providers, shows that compared to using the Centor Score, AI-based CDSS can improve clinicians’ predictions. Despite higher agreement with AI, participants report lower trust in its advice than in the Centor Score, leading to more requests for in-person confirmatory testing. Effectively integrating AI is crucial in the telehealth-based diagnosis of infectious diseases, given the implications of antibiotic over-prescriptions. We demonstrate that AI-based CDSS can improve the accuracy of remote strep throat screening yet underscores the necessity to enhance human–machine collaboration, particularly in trust and intelligibility. This ensures providers and patients can capitalize on AI interventions and smartphones for virtual healthcare. Strep pharyngitis, or strep throat, is a bacterial infection that can cause a sore throat. Artificial intelligence (AI) can use photos taken on a person’s phone to help diagnose strep throat, offering an additional way for doctors to screen patients during virtual appointments. However, it is currently unclear whether doctors will trust AI recommendations or how they might use them in decision-making. We surveyed clinicians about their use of an AI system for strep throat screening with smartphone images. We compared different ways of providing AI recommendations to standard medical guidelines. We found that all tested AI methods helped clinicians to identify strep throat cases. However, clinicians trusted AI less than their usual clinical guidelines, leading to more requests for follow-up in-person testing. Our results show how AI may improve the accuracy of pharyngitis assessment. Still, further research is needed to ensure doctors trust and collaborate with AI to improve remote healthcare. Gomez et al. develop an artificial intelligence-based clinical decision support tool that can help diagnose streptococcal pharyngitis using images obtained using a smartphone. Whilst the tool improves clinical prediction accuracy compared to the modified centor score, clinicians report lower trust in the advice and order more in-person tests.