Artificial Intelligence Among U.S. Hematology Oncology Fellows: A Multicenter Survey of Education, Attitudes, and Clinical Use

PURPOSE: A prior national survey of U.S. hematology/oncology (H/O) fellowship curricula demonstrated substantial heterogeneity and limited protected didactic time. Since then, artificial intelligence (AI), including large language models (LLM) and ambient tools, has become increasingly integrated into trainee education and clinical practice. We conducted a multi-center survey to assess the use of AI among H/O fellows. METHODS: H/O fellows were recruited via program leadership to complete an anonymous survey adapted from our prior study, with added questions on AI education, attitudes, and clinical use. Responses were collected via REDCap and summarized using descriptive statistics. RESULTS: A total of 118 H/O fellows responded from 18 of 30 invited U.S. H/O fellowship programs (60%), primarily from academic centers (94%), with an even distribution across fellowship training years. Most fellows (74%) reported using AI tools. Other commonly used resources included NCCN guidelines (92%), UpToDate (86%). Only 8% reported receiving formal AI training. Most fellows viewed AI as useful for education (93%) and were confident using it for learning (74%); 92% anticipated increased use and 82% desired formal training. LLMs were most commonly used to clarify concepts (86%), summarize literature (83%), and explore emerging research (75%). AI-assisted documentation was the most frequent clinical application (51%). Reported barriers included (in order of highest concern) accuracy, lack of formal training, data privacy, and unclear ethical or institutional guidelines. CONCLUSIONS: AI is widely used and valued by current H/O fellows, yet formal training during fellowship remains limited. These findings highlight the need for structured education on effective, safe, and ethical AI use to support clinical integration.