Faculty Bibliography

Penicillin allergy labels are common and clinically consequential. Approximately 10% of patients report a penicillin allergy, yet most are not truly allergic and can safely receive beta-lactams. In the inpatient setting, these labels drive use of broader-spectrum agents, contributing to increased healthcare-associated infections, antimicrobial resistance, longer hospital stays, and higher costs. Recent evidence supports delabeling as a key antimicrobial stewardship strategy. The PEN-FAST clinical decision rule enables identification of low-risk patients suitable for streamlined evaluation. The PALACE randomized trial demonstrated that, among low-risk adults, direct oral penicillin challenge is noninferior to traditional skin testing and associated with very low rates of adverse reactions. This narrative review summarizes current evidence on epidemiology, mechanisms, risk stratification, and delabeling strategies, and provides practical recommendations for integrating penicillin allergy delabeling into routine clinical practice.

Artificial intelligence (AI) has rapidly emerged as a transformative force in radiology, offering enhanced diagnostic accuracy, workflow optimization, and the potential to alleviate rising imaging demands. As radiology remains inherently dependent on pattern recognition and high-volume data interpretation, it represents an ideal domain for AI integration. This narrative review synthesizes current evidence on the clinical impact of AI across multiple dimensions of radiologic practice, including diagnostic performance, workflow efficiency, patient perspectives, and trainee education. AI systems have demonstrated performance approaching or exceeding that of radiologists in high-prevalence tasks, particularly in chest imaging and breast cancer screening, while also improving triage and reducing report turnaround times. However, these benefits are accompanied by significant challenges. Automation bias, over-reliance on algorithmic output, and anthropomorphic framing may compromise clinical judgment. Additionally, AI integration may paradoxically increase workload and contribute to radiologist burnout when poorly implemented. Patient-centered studies consistently indicate a preference for AI-augmented, rather than autonomous, diagnostic models, underscoring the enduring importance of physician oversight and communication. Among trainees, concerns regarding job security persist, though these are mitigated by increased AI literacy and structured educational initiatives. Ultimately, AI is best conceptualized not as a replacement for radiologists, but as a complementary tool. Thoughtful integration, combined with robust training, validation, and human oversight, will be essential to ensure that AI enhances rather than diminishes the quality of radiologic care.