Bin Jalal, Arif Hanafi;
Ngai, Victoria;
Hanrahan, John Gerrard;
Das, Adrito;
Khan, Danyal Z;
Cotton, Elizabeth;
Sharela, Shazia;
... Pandit, Anand S; + view all
(2024)
Empowering Early Career Neurosurgeons in the Critical Appraisal of Artificial Intelligence and Machine Learning: The Design and Evaluation of a Pilot Course.
World Neurosurgery
, 190
e537-e547.
10.1016/j.wneu.2024.07.166.
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Abstract
Background: Artificial intelligence (AI) is expected to play a greater role in neurosurgery. There is a need for neurosurgeons capable of critically appraising AI literature to evaluate its implementation or communicate information to patients. However, there are a lack of courses delivered at a level appropriate for individuals to develop such skills. We assessed the impact of a 2-day (non-credit bearing) online digital literacy course on the ability of individuals to critically appraise AI literature in neurosurgery. Methods: We performed a prospective, quasi-experimental non-randomized, controlled study with an intervention arm comprising individuals enrolled in our 2-day digital health literacy course and a waiting-list control arm used for comparison. We assessed participants' pre- and post-course knowledge, confidence, and course acceptability using Qualtrics surveys designed for the purpose of this study. Results: A total of 62 individuals (33 participants, 29 waitlist controls), including neurosurgical trainees and both undergraduate and post-graduate students, attended the course and completed the pre-course survey. The 2 groups did not vary significantly in terms of age or demographics. Following the course, participants significantly improved in their knowledge of AI (mean difference = 3.86, 95% CI = 2.97–4.75, P-value < 0.0001) and confidence in critically appraising literature using AI (P-value = 0.002). Similar differences in knowledge (mean difference = 3.15, 95% CI = 1.82–4.47, P-value < 0.0001) and confidence (P-value < 0.0001) were found when compared to the control group. Conclusions: Bespoke courses delivered at an appropriate level can improve clinicians' understanding of the application of AI in neurosurgery, without the need for in-depth technical knowledge or programming skills.
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