What is the knowledge about artificial intelligence in medical imaging among radiographer?
Best insight from top research papers
Radiographers generally have fair knowledge about artificial intelligence (AI) and its prospects in medical imaging but lack in-depth knowledge, as indicated by studies in Ghana, Saudi Arabia, Sudan, and Yemen . While a significant portion of radiographers are aware of AI's incorporation into imaging modalities, there is a lack of awareness regarding AI's emergence in radiography practice in specific regions . Additionally, radiographers exhibit varying levels of knowledge, attitudes, and practices towards AI, with some showing interest in learning more about AI applications in medical imaging . The studies also highlight the need for educational policies to integrate AI education into the medical imaging curriculum to prepare radiographers for the future and practical exposure to AI technology in imaging equipment .
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
03 Feb 2023 | Radiologists and residents in Kenya have basic knowledge of AI in medical imaging, with limited understanding of related concepts and low utilization in daily practice. |
| Radiographers have varying knowledge levels of AI in medical imaging, with senior doctors showing higher awareness (4.88/10) compared to junior doctors (3.68/10). Overall, support for AI in skeletal radiography is strong. | |
| Radiographers have knowledge of artificial intelligence, particularly machine learning and deep learning, applied in diagnostic imaging, facing challenges like labeling, overfitting, and privacy concerns. | |
| Radiography students in Ghana have fair knowledge but lack in-depth understanding of AI in medical imaging, with limited awareness of AI's emergence in radiography practice. | |
| Radiographers have less knowledge about artificial intelligence in medical imaging compared to radiologists, indicating a significant knowledge gap between the two groups. |
Related Questions
1.How does artificial intelligence improve the accuracy and efficiency of medical imaging and diagnostics?4 answersArtificial intelligence (AI) significantly enhances the accuracy and efficiency of medical imaging and diagnostics. AI, particularly through machine learning and deep learning algorithms, aids in precise disease diagnosis by analyzing various medical imaging modalities like MRI, X-ray, and CT scans with expert-level accuracy. AI systems can detect conditions such as lung cancer, cardiovascular diseases, and skin cancer more effectively than human experts, leading to earlier and more accurate diagnoses. Additionally, AI enables personalized treatment strategies by analyzing patient data to tailor therapies, revolutionizing disease management through precision medicine. AI also enhances clinical decision-making by processing vast amounts of data to provide evidence-based recommendations, ultimately improving patient care and outcomes. Overall, AI's integration into medical imaging and diagnostics showcases its potential to revolutionize healthcare by improving accuracy, efficiency, and patient outcomes.
What is the knowledge about artificial intelligence in medical imaging among radiographer student?5 answersRadiographer students generally have a fair knowledge of artificial intelligence (AI) in medical imaging but lack in-depth understanding. Studies in Ghana, Saudi Arabia, Sudan, Yemen, the UK, and Spain show varying levels of awareness and knowledge among radiographer students. While a significant percentage of students are aware of AI's incorporation into imaging modalities and its emergence in medical imaging, they often lack awareness of AI's gradual emergence in radiography practice. Additionally, students express interest in learning more about AI applications in medical imaging, but many feel threatened or unsure about job security due to AI technology. There is a consensus on the need for educational policies integrating AI education into the medical imaging curriculum to prepare students for the future and practical exposure to AI technology in medical imaging equipment.
Are there successful implementations of artificial intelligence applications in radiology?5 answersSuccessful implementations of artificial intelligence (AI) applications in radiology have been reported. AI has been shown to have the potential to complement case interpretation, improve quality and safety, optimize workflow, increase efficiency, and enhance patient satisfaction. Diagnostic applications of AI in radiology are more widely understood and used, with significant potential for growth. AI has also been applied in emergency radiology to identify common conditions, improve reporting speed, and assist with non-interpretive tasks such as protocolling and workflow prioritization. However, there are still barriers to the widespread adoption of AI in radiology, including concerns about its value, return on investment, and the need for regulation to guide implementation. Despite these challenges, AI in radiology shows great promise for improving patient outcomes and relieving difficulties faced by radiologists.
What are the factors that influence radiologist's knowledge about artificial intelligence in diagnostic radiology?4 answersFactors that influence radiologists' knowledge about artificial intelligence in diagnostic radiology include their level of expertise and readiness to learn and apply AI technology in clinical settings. Senior consultant radiologists tend to have better knowledge of AI compared to junior residents. Limited access to resources and insufficient preparation for the effective use of AI in practice can be barriers to AI education. National organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI, which can help trainees explore the creation, deployment, and critical evaluation of AI applications. Additionally, the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and clinical case volume can also influence radiologists' knowledge of AI.
How is radiologist's knowledge about artificial intelligence in diagnostic radiology related to their clinical practice?5 answersRadiologists' knowledge about artificial intelligence (AI) in diagnostic radiology is related to their clinical practice in several ways. Firstly, AI has the potential to complement case interpretation and aid in non-interpretive aspects of radiological work, such as patient scheduling and communication of imaging information. However, there are barriers to the adoption of AI in the clinic, with many radiologists still unconvinced of its value and return on investment. Additionally, radiologists need to be aware of cybersecurity concerns specific to AI in radiology, as the integration of AI projects involves numerous devices and data warehouses that can be vulnerable to cyber threats. Radiologists also need to consider practical aspects when assessing and deploying AI tools in their practice, ensuring that resources are properly allocated and there is an appropriate return on investment in terms of patient care, safety, and process efficiency. Overall, radiologists' knowledge about AI in diagnostic radiology is crucial for effectively incorporating AI into their clinical practice and addressing the challenges and opportunities it presents.
What are the challenges and opportunities for using artificial intelligence in radiology education?5 answersArtificial intelligence (AI) has the potential to enhance radiology education by improving the learning experience for trainees and providing personalized education. However, there are challenges that need to be addressed. Limited access to resources and insufficient preparation hinder effective use of AI in practice. To overcome these barriers, national organizations have sponsored formal and self-directed learning courses on imaging informatics and AI. Additionally, the implementation of AI in radiology education requires accurate datasets with large sample sizes and appropriate labels, as well as ethical considerations. Despite these challenges, it is crucial to continue training radiologists and teach them about AI to fully exploit its potential. AI education should start in medical school, be reinforced during residency programs, and be maintained through continuing medical education. By addressing these challenges and embracing the opportunities, AI can significantly contribute to radiology education.