AI in oncology: Resident perspectives by gender and tech literacy.

• DOI: 10.1200/jco.2024.42.16_suppl.9055
• Publication Date: 2024-06-26T17:03:52Z
• Authors: Fernanda Malucell...; Laura Collie; Thomas Kennedy; Jacqueline J. Nabhen; Amir Safavi; Giovanni G Cerri; Fabio Ynoe de Moraes

9055 Background: The integration of AI and ML in oncology demands that physicians adapt and grasp the basics for responsible use. We evaluated AI knowledge and perspectives among Canadian oncology residents, noting differences by program, gender, and tech literacy, and pinpointing education gaps. Methods: An ethics-approved survey collected anonymous responses from Canadian oncology residents, analyzing gender, program, and tech skills. Descriptive statistics, cross-tabs, and chi-square tests assessed associations; t-tests and Mann-Whitney tests compared groups. Results: A total of 57 (31%) residents and fellows, out of an estimated 182, participated, with representation from each oncology training program in Canada. Most of participants were male (63.2%) and most participants self-identified as white (42.1%) or Asian (22.8%). RO programs were better represented than MO programs (68.5% vs 31.6%) with balanced representation across all years of training. In our survey, women equally favored Medical Oncology (MO) and Radiation Oncology (RO) at 50%, but were more in MO (55.6%) than RO (26.3%). Men preferred RO (77.8%) over MO (22.2%). Tech literacy showed a gender gap, with more men (91.7%) feeling tech-savvy than women (8.3%). Tech-savvy respondents leaned towards RO (84%) and were younger (30 vs 33 years). They also showed more willingness to use AI, with a significant difference in willingness scores (1.52 vs 2.06). Despite gender not influencing AI attitudes significantly, tech literacy correlated with better AI understanding. High awareness of AI in medicine was reported (91.2%), with a strong belief in AI's future prevalence (96%). The majority were willing to use AI (86%) and recognized the need to understand it (74%). RO participants were more inclined towards AI education and usage than MO counterparts. The study highlighted a significant interest in AI learning (73%), with a preference for workshops (79%). Only 29% could describe AI, indicating a gap in AI education, despite 63% acknowledging its importance in training. Formal AI training was rare (12.3%), with a desire for more education, especially among RO residents. All findings had p<0.05. Conclusions: Canadian oncology residents anticipate AI's growing influence in medicine but face educational deficiencies. Gender, program preference, and tech literacy impact attitudes toward AI, highlighting the need for inclusive education to bridge gaps and foster diversity in AI's medical application.