Purpose: Artificial intelligence (Al) use in medicine raises ethical concerns regarding transparency, accountability, confidentiality, and academic integrity. Few studies have examined AI ethics literacy, and existing AI literacy studies report minimal gains in ethical preparedness. We assessed perceived preparedness for AI ethics using vignettes to identify gaps in our current training. Methods: Data were analyzed from a cross-sectional survey of medical students and faculty at SUNY Downstate Health Sciences University. Part one assessed prior AI training and perception of medical school preparedness for AI ethics using descriptive frequencies and chi-square tests. Part two assessed participant preparedness using bioethicist reviewed, AI-focused clinical vignettes based on the AMA’s Principles of Medical Ethics. Participants reported perceived ability to apply beneficence, nonmaleficence, autonomy, and justice to the vignettes using a 4-point Likert scale. Median preparedness scores were calculated and compared between groups using Mann–Whitney U tests. Results: In part one (26 medical students, 21 faculty), 89.4% of participants reported no formal AI training (χ²=2.824, p=0.093), with no significant differences between students and faculty. 89.4% felt medical school does not prepare students to address AI ethics, with students being more likely to report inadequate preparation (χ²=6.927, p=0.008). In part two (21 students, 18 faculty), the overall median preparedness score was 2.44 (IQR 1.78–2.94), with students scoring 2.33 (IQR 1.78–2.56) and faculty 2.83 (IQR 2.00–3.67). Faculty reported greater perceived preparedness (Mann-Whitney U: Z = -2.172, p = 0.03). Conclusion: Students and faculty reported that medical training inadequately prepares learners for AI ethics, and median scores suggested limited confidence in applying ethical principles to AI-related scenarios. These findings support integrating AI scenarios into existing ethics curricula.