Artificial intelligence in radiology, nuclear medicine and radiotherapy: Perceptions, experiences and expectations from the medical radiation technologists in Central and South America.
Authors
Affiliations (10)
Affiliations (10)
- Universidad de Costa Rica, School of Health Technologies, Faculty of Medicine, San Jose, Costa Rica.
- Department of Radiography, School of Health and Medical Sciences, City St. George's, University of London, London, UK.
- Department of Medical Technology, Faculty of Medicine, Universidad de Chile, Chile.
- Dep. Tecnicatura Universitaria en Diagnóstico por Imágenes, Escuela de Técnicos Asistenciales en Salud, Universidad Nacional de Cuyo, Argentina.
- Departamento de Imagenología y Radiología, Facultad de Ciencias Médicas, Universidad Central de Ecuador, Ecuador.
- Facultad de Ciencias de la Salud y del Deporte, Fundación Universitaria del Área Andina, Colombia.
- Escuela de Tecnología Médica, Universidad Católica del Maule, Chile.
- Centro Universitario Lusiada, Universidad Sao Judas Tadeu, Sao Paulo, , Brasil.
- Departamento de Imagenología, Facultad de Medicina, Universidad de la República, Uruguay.
- Universidad de Costa Rica, School of Health Technologies, Faculty of Medicine, San Jose, Costa Rica. Electronic address: [email protected].
Abstract
Artificial intelligence (AI) has been growing in the field of medical imaging and clinical practice. It is essential to comprehend the perceptions, experiences, and expectations regarding AI implementation among medical radiation technologists (MRTs) working in radiology, nuclear medicine, and radiotherapy. Some global studies tend to inform about AI implementation, but there is almost no information from Central and South American professionals. This study aimed to understand the perceptions of the impact of AI on the MRTs, as well as the varying experiences and expectations these professionals have regarding its implementation. An online survey was conducted among Central and South American MRTs for the collection of qualitative data concerning the primary perceptions regarding the implementation of AI in radiology, nuclear medicine, and radiotherapy. The analysis considered descriptive statistics in closed-ended questions and dimension codification for open-ended responses. A total of 398 valid responses were obtained, and it was determined that 98.5 % (n = 392) of the respondents agreed with the implementation of AI in clinical practice. The primary contributions of AI that were identified were the optimization of processes, greater diagnostic accuracy, and the possibility of job expansion. On the other hand, concerns were raised regarding the delay in providing training opportunities and limited avenues for learning in this domain, the displacement of roles, and dehumanization in clinical practice. This sample of participants likely represents mostly professionals who have more AI knowledge than others. It is therefore important to interpret these results with caution. Our findings indicate strong professional confidence in AI's capacity to improve imaging quality while maintaining patient safety standards. However, user resistance may disturb implementation efforts. Our results highlight the dual need for (a) comprehensive professional training programs and (b) user education initiatives that demonstrate AI's clinical value in radiology. We therefore recommend a carefully structured, phased AI implementation approach, guided by evidence-based guidelines and validated training protocols from existing research. AI is already present in medical imaging, but its effective implementations depend on building acceptance and trust through education and training, enabling MRTs to use it safely for patient benefit.