| Title: |
Knowledge and Use of Artificial Intelligence Among Oncology Faculty and Trainees at a Comprehensive Cancer Center in 2025. |
| Authors: |
Schadler, Keri; Klopp, Ann; Kouzy, Ramez; Krishnamani, Pavitra P.; Filson, Rebekah; Moody, Aiden; Grubbs, Elizabeth; Munoz, Nicole; Payne, Lauren; Stapleton, Shawn; Chung, Caroline |
| Source: |
JCO Clinical Cancer Informatics; 5/27/2026, Vol. 10, p1-9, 9p |
| Subject Terms: |
Artificial intelligence; Oncology; Data visualization; Machine learning; Medical education; Medical school faculty; Cancer diagnosis |
| Abstract: |
PURPOSE: Artificial intelligence (AI) has been used in medicine for decades, but recent advances in machine learning and large language models have rapidly expanded its accessibility and applications in oncology. Although AI offers efficiency in data analysis, synthesis, and communication, important questions remain regarding output reliability, research rigor, data security, and appropriate reliance on automated tools, underscoring the need for thoughtful implementation and training. METHODS: To better understand current AI use in academic oncology and perceptions about utility, we conducted a survey of faculty and trainees at a comprehensive cancer center. RESULTS: Among 227 respondents (55% women; 64% clinical faculty), 58% reported using AI several times per month or more, whereas 15% had never used it. The most common applications were summarizing academic or research information and generating data visualizations. Attitudes toward AI were generally positive: 74% agreed that AI will improve cancer diagnosis within the next decade. In contrast, views were more cautious regarding end-of-life decision making, with 35% disagreeing that AI would be beneficial in that context. Despite broad interest, a substantial training gap emerged. Nearly 93% of respondents endorsed the need for dedicated AI training, and approximately half reported not knowing where to find reliable learning resources. Lower AI use was associated with female gender and age over 60 years. CONCLUSION: AI use among oncology faculty and trainees is common but variable, with differences across demographic and professional groups. These findings highlight the need for structured, accessible training and institutional guidance to promote appropriate, equitable, and high-quality integration of AI into oncology research and clinical care. [ABSTRACT FROM AUTHOR] |
| : |
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| Database: |
Complementary Index |