My published work spans three connected areas: the governance and accountability structures that hold AI systems answerable, the regulation of AI-enabled clinical software itself, and the practical realities of implementing AI safely in clinical workflows.
Loufek B, Vidal D, McClintock DS, et al. Embedding Internal Accountability Into Health Care Institutions for Safe, Effective, and Ethical Implementation of Artificial Intelligence Into Medical Practice: A Mayo Clinic Case Study. Mayo Clin Proc Digit Health. 2024;2(4):574–583.
A case study of the internal accountability model Mayo Clinic built to govern AI implementation across clinical practice.
Sendak MP, et al., Loufek B, et al. Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety. J Am Med Inform Assoc. 2024;31(7):1622–1627.
Examines how health delivery organizations balance regulatory compliance with patient-safety obligations as AI adoption scales.
Vidal DE, Loufek B, Kim YH, Vidal NY. Navigating US Regulation of Artificial Intelligence in Medicine—A Primer for Physicians. Mayo Clin Proc Digit Health. 2023;1(1):31–39.
A primer orienting physicians to the US regulatory landscape governing AI-enabled medical technologies.
Corso MM, Kim PT, Loufek BT, et al. Bridging the gap: aligning clinical decision support regulation with clinical practice in the era of artificial intelligence. Lancet Digit Health. 2026;8(3):100964.
Argues for aligning clinical decision support regulation more closely with how AI is actually used in practice.
Korfiatis P, Kline TL, et al., Loufek BT, et al. Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations. Mayo Clin Proc Digit Health. 2025;3(1):100188.
Reviews the practical challenges of embedding AI algorithms into live radiology workflows.
Additional publications, presentations, academic contributions, and FDA interactions available upon request.