Canadian oncology residents’ knowledge of and attitudes towards artificial intelligence and machine learning.
Bibliographic record
Abstract
e13587 Background: The use of artificial intelligence (AI) and machine learning is becoming more common and is expected to expand further in order to meet the needs of our ever-evolving healthcare system. In oncology, AI and machine learning are already being explored in various applications. Despite AI’s importance, there is sparse formal teaching on AI incorporated into medical schools’ curricula and residency training programs. In this study, we examined the perceptions and knowledge of Canadian oncology residents and fellows with respect to AI technologies. Methods: An electronic, anonymous, questionnaire-based survey was distributed to residents and fellows in medical and radiation oncology programs across Canada. Survey questions spanned areas of demographics, familiarity with AI, personal attitudes towards AI, and perspectives regarding AI use in different specialties. Approval was obtained from the Queen’s Research Ethics Board prior to conducting this study. Mixed-methods statistical analysis is ongoing. Qualitative data will be analyzed using thematic analysis. Univariable and multivariable regressions will be conducted to identify any correlation between perception or knowledge of AI and demographic factors. Results: Fifty-seven participants responded in total. Most residents (67%) agreed or strongly agreed that it was important they learn about AI. Seventy percent indicated that, if given the chance, they would like to learn more about AI, yet the majority of participants (88%) indicated they had not received formalized teaching. Disciplines that were felt to be most associated with AI were radiology (98%), radiation oncology (84%), and pathology (58%). With respect to the field of radiation oncology, 98% of respondents felt that AI had the potential to replace some, most, or all medical activities. A perceived barrier to understanding AI was a lack of knowledge of mathematics and programming (63%). Respondents indicated that their preferred formats for learning about AI would be workshops (78%), lectures (60%), and collaborative activities with other departments (46%). Conclusions: Our results show that Canadian oncology residents’ sense that AI is important and relevant to their area of training. Despite this, they have not received education on these topics. Thus, formalized teaching, such as lectures and workshops, would be perceived as beneficial by most Canadian oncology residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".