{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T11:53:04Z","timestamp":1791978784617,"version":"4.4.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Clinician trust in artificial intelligence (AI) is a frequently cited barrier to adoption, yet there is limited empirical evidence on how transparency features influence trust. This study aimed to address this evidence gap.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We conducted a randomized controlled experiment in which 220 clinicians were randomized to 1 of 3 conditions: no transparency (control), numeric confidence scores, or narrative explanations with citations, using simulated AI diagnostic assistance across clinical case vignettes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Transparency did not influence tool-specific trust [F(2, 214)\u2009=\u20090.38, P\u2009=\u2009.68], global trust [F(2, 217)\u2009=\u20090.13, P\u2009=\u2009.88], or perceived information credibility [F(2, 210)\u2009=\u20090.42, P\u2009=\u2009.66]. Confidence increased following any AI assistance (+6.7%), but narrative explanations were associated with greater change in confidence compared with both control (P\u2009=\u2009.024) and numeric confidence (P\u2009=\u2009.0051). Participants changed or refined diagnoses after reviewing AI output 45.8% (SD 23.4%) of the time, with participants in the control group demonstrating higher levels of behavioral influence than transparency conditions (P\u2009=\u2009.0006).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Trust, confidence, and behavioral influence represent distinct constructs and are impacted differently by transparency methods. The difference in these contributions to clinician-AI partnerships should be considered when designing for safe trust calibration, with particular consideration for the dissociation between trust and confidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest common interface transparency features alone failed to produce a detectable, meaningful shift in clinician trust in AI assistance and that changes may instead alter behavioral influence and confidence without a corresponding change in trust.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag176","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T11:40:39Z","timestamp":1791286839000},"source":"Crossref","is-referenced-by-count":0,"title":["Transparency features alone are insufficient for trust calibration in artificial intelligence diagnostic support"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1149-8713","authenticated-orcid":false,"given":"Cassiopeia","family":"Frank","sequence":"first","affiliation":[{"name":"School of Medicine, University of North Carolina , Chapel Hill, NC 27514,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4072-4498","authenticated-orcid":false,"given":"Alexander","family":"Stevens","sequence":"additional","affiliation":[{"name":"Principal AI Systems & Operations Technologist, DynaTouch , San Antonio, TX 78217,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2043-6461","authenticated-orcid":false,"given":"Yea-Jen","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205,","place":["United 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