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Although such solutions\u2019 potential remains largely untapped, providers\u2019 trust and understanding are vital for effective adoption. This study examines how different human\u2013AI interaction paradigms affect clinicians\u2019 responses to an emerging AI CDSS for streptococcal pharyngitis (strep throat) detection from smartphone throat images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019s effect on clinicians\u2019 accuracy, confirmatory testing rates, and perceived trust and understanding of the CDSS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study, involving 121 telehealth providers, shows that compared to using the Centor Score, AI-based CDSS can improve clinicians\u2019 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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\u2013machine collaboration, particularly in trust and intelligibility. This ensures providers and patients can capitalize on AI interventions and smartphones for virtual healthcare.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s43856-024-00568-x","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T07:05:22Z","timestamp":1721804722000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Explainable AI decision support improves accuracy during telehealth strep throat screening"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1536-3421","authenticated-orcid":false,"given":"Catalina","family":"Gomez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brittany-Lee","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alisa","family":"Zayas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0055-9950","authenticated-orcid":false,"given":"Mathias","family":"Unberath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9877-4028","authenticated-orcid":false,"given":"Therese","family":"Canares","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"568_CR1","doi-asserted-by":"crossref","unstructured":"Pacis, D. 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