Journal of medical imaging and radiation oncology · 2026
Awareness and Perception of Artificial Intelligence in Diagnostic Imaging: A Cross-Speciality Survey of Indian Medical Practitioners.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kassim Mwanajuma Yahya, Sharma Dolly A, Shah Shreeni, Panda Mamta, Ojha Santosh
Аннотация
INTRODUCTION: Artificial Intelligence (AI) is one of the rapidly evolving areas of diagnostic imaging; however, the extent to which medical professionals are willing to apply these tools to routine care in India has not been thoroughly researched. This is a cross-sectional descriptive study that evaluated awareness, perceptions and perceived impact of AI among radiologists, orthopaedic surgeons and physicians practising in Gujarat.
METHODS: A questionnaire, designed according to Scheetz et al., was completed by 102 practitioners in a structured manner. Frequency distributions, percentage analysis and descriptive statistics were performed.
RESULTS: It was also found that 88.2% were aware of AI, with only 47.1% identifying their knowledge as average, and 13.7% as above average. Although AI is not used regularly, 52.9% reported never using AI. Most respondents had a favourable view of AI: 66.7% indicated willingness to implement AI-supported clinical workflow, and 60.8% believed AI would positively affect their area. The issues raised were data security (67.6%), less dependence on specialists (62.7%) and confidence in the black-box systems. Most of the respondents (64.7%) felt that AI would have a significant effect on clinical practice in the next 1-10 years.
CONCLUSION: Radiologists expressed relatively more acceptance, and physicians expressed more concern with workforce implications. The results show that there is a significant interest, but unequal readiness, in the specialties. Enhancing AI-specific training and developing governance models can help make the safe and ethical adoption of AI-enabled diagnostic imaging.
METHODS: A questionnaire, designed according to Scheetz et al., was completed by 102 practitioners in a structured manner. Frequency distributions, percentage analysis and descriptive statistics were performed.
RESULTS: It was also found that 88.2% were aware of AI, with only 47.1% identifying their knowledge as average, and 13.7% as above average. Although AI is not used regularly, 52.9% reported never using AI. Most respondents had a favourable view of AI: 66.7% indicated willingness to implement AI-supported clinical workflow, and 60.8% believed AI would positively affect their area. The issues raised were data security (67.6%), less dependence on specialists (62.7%) and confidence in the black-box systems. Most of the respondents (64.7%) felt that AI would have a significant effect on clinical practice in the next 1-10 years.
CONCLUSION: Radiologists expressed relatively more acceptance, and physicians expressed more concern with workforce implications. The results show that there is a significant interest, but unequal readiness, in the specialties. Enhancing AI-specific training and developing governance models can help make the safe and ethical adoption of AI-enabled diagnostic imaging.