RESUMEN
RATIONALE AND OBJECTIVES: The objective of this study was to evaluate the effectiveness of a pilot artificial intelligence (AI) certificate program in aiding radiology trainees to develop an understanding of the evolving role and application of artificial intelligence in radiology. A secondary objective was set to determine the background of residents that would most benefit from such training. MATERIALS AND METHODS: This was a prospective pilot study involving 42 radiology residents at two separate residency programs who participated in the Radiological Society of North America Imaging AI Foundational Certificate course over a four-month period. The course consisted of 6 online modules that contained didactic lectures followed by end-of-module quizzes to assess knowledge gained from these lectures. Pre- and post-course assessments were conducted to evaluate the residents' knowledge and skills in AI. Additionally, a post-course survey was performed to assess participants' overall satisfaction with the course. RESULTS: All participating residents completed the certificate program. The mean pre-course assessment score was 37 %, which increased to 73 % after completing the modules (p < 0.001). 74 % (31/42) endorsed the belief the course improved familiarity with artificial intelligence in radiology. Residency program, residency year, and reported prior familiarity with AI were not found to influence pre-course score, post-course score, nor score improvement. 57 % (24/42) endorsed interest in pursuing further certification in AI. CONCLUSION: Our pilot study suggests that a certificate course can effectively enhance the knowledge and skills of radiology residents in the application of AI in radiology. The benefits of such a course can be found regardless of program, resident year, and self-reported prior resident understanding of radiology in AI.
RESUMEN
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
