Enhancing AI-Clinician Interaction: Building Trust to Improve Patient Outcomes

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dc.contributor.authorNassarian, Elhamen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2025-07-25T17:19:29Zen
dc.date.available2025-07-25T17:19:29Zen
dc.date.issued2025-04-14en
dc.description.abstractThis research systematically reviews interpretable machine learning (IML) and explainable AI (XAI) in healthcare, proposing a Responsible Clinician-AI Collaboration Framework. It introduces a novel three-level interpretability process and step-by-step roadmap to enhance AI-clinician communication, fostering trust and improving decision-making in clinical decision support systems (CDSS).en
dc.description.sponsorshipCollege of Engineeringen
dc.format.extentDimensions: 1920 × 1080en
dc.format.extentDuration: 00:04:55en
dc.format.extentSize: 11.5 MBen
dc.format.mimetypevideo/mp4en
dc.format.mimetypetext/vtten
dc.identifier.urihttps://hdl.handle.net/10919/136912en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.ispartofseries2025 Paul E. Torgersen Award Winnersen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectIndustrial and Systems Engineeringen
dc.titleEnhancing AI-Clinician Interaction: Building Trust to Improve Patient Outcomesen
dc.typeVideoen
dc.typePresentationen
dc.type.dcmitypeMovingImageen

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