{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T23:48:32Z","timestamp":1791935312238,"version":"4.3.4"},"reference-count":56,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"\n Background<\/jats:title>\n As the US Food and Drug Administration (FDA)\u2013approved use of artificial intelligence (AI) for medical imaging rises, radiologists are increasingly integrating AI into their clinical practices. In lung cancer screening, diagnostic AI offers a second set of eyes with the potential to detect cancer earlier than human radiologists. Despite AI\u2019s promise, a potential problem with its integration is the erosion of patient confidence in clinician expertise when there is a discrepancy between the radiologist\u2019s and the AI\u2019s interpretation of the imaging findings.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We examined how discrepancies between AI-derived recommendations and radiologists\u2019 recommendations affect patients\u2019 agreement with radiologists\u2019 recommendations and satisfaction with their radiologists. We also analyzed how patients\u2019 medical maximizing-minimizing preferences moderate these relationships.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted a randomized, between-subjects experiment with 1606 US adult participants. Assuming the role of patients, participants imagined undergoing a low-dose computerized tomography scan for lung cancer screening and receiving results and recommendations from (1) a radiologist only, (2) AI and a radiologist in agreement, (3) a radiologist who recommended more testing than AI (ie, radiologist overcalled AI), or (4) a radiologist who recommended less testing than AI (ie, radiologist undercalled AI). Participants rated the radiologist on three criteria: agreement with the radiologist\u2019s recommendation, how likely they would be to recommend the radiologist to family and friends, and how good of a provider they perceived the radiologist to be. We measured medical maximizing-minimizing preferences and categorized participants as maximizers (ie, those who seek aggressive intervention), minimizers (ie, those who prefer no or passive intervention), and neutrals (ie, those in the middle).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Participants\u2019 agreement with the radiologist\u2019s recommendation was significantly lower when the radiologist undercalled AI (mean 4.01, SE 0.07, P<.001) than in the other 3 conditions, with no significant differences among them (radiologist overcalled AI [mean 4.63, SE 0.06], agreed with AI [mean 4.55, SE 0.07], or had no AI [mean 4.57, SE 0.06]). Similarly, participants were least likely to recommend (P<.001) and positively rate (P<.001) the radiologist who undercalled AI, with no significant differences among the other conditions. Maximizers agreed with the radiologist who overcalled AI (\u03b2=0.82, SE 0.14; P<.001) and disagreed with the radiologist who undercalled AI (\u03b2=\u20130.47, SE 0.14; P=.001). However, whereas minimizers disagreed with the radiologist who overcalled AI (\u03b2=\u20130.43, SE 0.18, P=.02), they did not significantly agree with the radiologist who undercalled AI (\u03b2=0.14, SE 0.17, P=.41).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Radiologists who recommend less testing than AI may face decreased patient confidence in their expertise, but they may not face this same penalty for giving more aggressive recommendations than AI. Patients\u2019 reactions may depend in part on whether their general preferences to maximize or minimize align with the radiologists\u2019 recommendations. Future research should test communication strategies for radiologists\u2019 disclosure of AI discrepancies to patients.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/68823","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T07:56:23Z","timestamp":1743753383000},"page":"e68823","source":"Crossref","is-referenced-by-count":6,"title":["Patient Reactions to Artificial Intelligence\u2013Clinician Discrepancies: Web-Based Randomized Experiment"],"prefix":"10.2196","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6660-7671","authenticated-orcid":false,"given":"Farrah","family":"Madanay","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3283-6542","authenticated-orcid":false,"given":"Laura 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