{"id":21440,"date":"2024-12-06T12:20:29","date_gmt":"2024-12-06T17:20:29","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=21440"},"modified":"2024-12-06T14:23:42","modified_gmt":"2024-12-06T19:23:42","slug":"building-doctors-trust-in-ai","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2024\/12\/building-doctors-trust-in-ai\/","title":{"rendered":"Building Doctors\u2019 Trust in AI"},"content":{"rendered":"\"Illustration<\/a>\n

While artificial intelligence-powered systems can improve clinicians’ diagnostic accuracy during telehealth visits, doctors still don\u2019t fully trust algorithms to screen patients. That\u2019s according to a recent study by Johns Hopkins researchers, which they say highlights the need to improve human-AI collaboration.<\/p>\n

\u201cPhysicians do better with AI assistance, yet they still hesitate to alter their practices significantly as a result. For example, they will still frequently ask telehealth patients to visit the clinic for definitive testing,\u201d says Mathias Unberath<\/a>, John C. Malone Associate Professor of Computer Science<\/a> and a member of the team whose study appeared in Nature Communications Medicine<\/a><\/em>.<\/p>\n

Unberath and Therese Canares, a Johns Hopkins emergency medicine physician, used a smartphone-based AI system they developed (CurieDx) to examine \u201cexplainable\u201d AI\u2019s potential to enhance clinicians\u2019 trust in AI-driven diagnostic tools. They specifically focused on how doctors use and perceive the system\u2019s explanations of strep throat diagnoses, which rely on analyzing smartphone images of users\u2019 throats.<\/p>\n

The researchers created mockups featuring techniques the AI system might use to explain its diagnoses, including highlighting key visual features and providing examples of images it already analyzed and accurately diagnosed as either strep or not.<\/p>\n

The team had clinicians review the mockups, measuring their agreement with CurieDx\u2019s resulting diagnostic recommendations, perceived trust in the system, and how the explanations influenced physicians\u2019 recommendations. \u201cWe found that explaining by example was the most promising method,\u201d says lead author Catalina Gomez, a PhD student in computer science.<\/p>\n

The researchers theorized that this type of explanation was the most successful because it most closely mirrors human clinical reasoning, which involves incorporating prior experience in the analysis of a patient\u2019s condition.<\/p>\n

\u201cThis kind of explanation improved the accuracy of the clinicians\u2019 decisions. The providers also trusted the AI-generated predictions just as much as they trusted the diagnoses produced by their customary clinical prediction rule, which acted as our baseline,\u201d says Gomez.<\/p>\n

Even so, the clinicians in the study often felt it was necessary to ask patients diagnosed remotely to visit the clinic for a follow-up. \u201cThis opens the dialogue to examine clinical workflows that incorporate AI screening tools,\u201d says Canares.<\/p>\n

The team plans to continue exploring explainable AI methods to help users better understand how AI-powered systems work and further enhance user trust in those systems.<\/p>\n","protected":false},"excerpt":{"rendered":"

Johns Hopkins study finds doctors benefit from AI in telehealth but hesitate to fully trust algorithms, highlighting need for improved AI explanations.<\/p>\n","protected":false},"author":29,"featured_media":21443,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[24],"tags":[],"class_list":["post-21440","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-impact","issue-winter-2025"],"acf":[],"yoast_head":"\nBuilding Doctors\u2019 Trust in AI - JHU Engineering Magazine<\/title>\n<meta name=\"description\" content=\"Johns Hopkins researchers explore how explainable AI can improve clinician trust in telehealth diagnostics, highlighting ongoing challenges in human-AI collaboration.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/2024\/12\/building-doctors-trust-in-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Building Doctors\u2019 Trust in AI - 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