Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources
Diagn Interv Radiol
; 30(3): 163-174, 2024 05 13.
Article
en En
| MEDLINE
| ID: mdl-38145370
ABSTRACT
Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Radiología
/
Inteligencia Artificial
/
Instrucción por Computador
/
Radiólogos
Límite:
Humans
Idioma:
En
Revista:
Diagn Interv Radiol
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
RADIOLOGIA
Año:
2024
Tipo del documento:
Article