Bressem, Keno
Mayo Clinic
School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Citations 2
Agentic AI in Radiology: Evolution from Large Language Models to Future Clinical Integration
Khosravi, Bardia , Rouzrokh, Pouria, D’Antonoli, Tugba Akinci, Moassefi, Mana , Faghani, Shahriar, Mansuri, Aawez , Bressem, Keno K. , Tejani, Ali S. , Gichoya, Judy Wawira , Rouzrokh, Pouria , Khosravi, Bardia , Faghani, Shahriar , D’Antonoli, Tugba Akinci , Rouzrokh, Pouria , Akinci D'Antonoli, Tugba , Faghani, Shahriar , Bressem, Keno , Tejani, Ali , Gichoya, Judy W. , Moassefi, Mana
Radiology: Artificial Intelligence | January 14, 2026 | Volume: 8 | Issue: 2 | Pages: No page available
Agentic artificial intelligence marks a shift from large language models to autonomous, goal-driven clinical assistants. The framework presented in this report outlines implementation in radiology workflows and addresses cybersecurity, bias, and accountability challenges.
Cybersecurity Threats and Mitigation Strategies for Large Language Models in Health Care
D’Antonoli, Tugba Akinci , Tejani, Ali S. , Khosravi, Bardia , Blüthgen, Christian , Busch, Felix , Bressem, Keno K. , Adams, Lisa C. , Moassefi, Mana , Faghani, Shahriar , Gichoya, Judy Wawira , Akinci D'Antonoli, Tugba , Bluethgen, Christian , Gichoya, Judy W. , Khosravi, Bardia , Faghani, Shahriar , Bressem, Keno , Moassefi, Mana
Radiology: Artificial Intelligence | May 14, 2025 | Volume: 7 | Issue: 4 | Pages: No page available
Large language models pose unique cybersecurity risks in health care, including vulnerability to malicious attacks and data breaches. This article equips professionals with strategies to mitigate these threats for safe use.