Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting
Authors: Wuni, A.R., Botwe, B.O. and Akudjedu, T.N.
Journal: Radiography
Volume: 27
Pages: S69-S73
eISSN: 1532-2831
ISSN: 1078-8174
DOI: 10.1016/j.radi.2021.07.021
Abstract:Objectives: Current trends in clinical radiography practice include the integration of artificial intelligence (AI) and related applications to improve patient care and enhance research. However, in low resource countries there are unique barriers to the process of AI integration. Using Ghana as a case study, this paper seeks to discuss the potential impact of AI on future radiographic practice in low-resource settings. The opportunities, challenges and the way forward to optimise the potential benefits of AI in future practice within these settings have been explored. Key findings: Some of the barriers to AI integration into radiographic practice relate to lack of regulatory and legal policy frameworks and limited resource availability including unreliable internet connectivity and low expert skillset. Conclusion: These barriers notwithstanding, AI presents a great potential to the growth of medical imaging and subsequently improving quality of healthcare delivery in the near future. For example, AI-enabled radiographer reporting has a potential to improving quality of healthcare, especially in low-resource settings like Ghana with an acute shortage of radiologists. In addition, futuristic AI-enabled advancements such as synthetic cross-modality transfer where images from one modality are used as a baseline to generate a corresponding image of another modality without the need for additional scanning will be of particular benefit in low-resource settings. Implications for practice: The urgent need for inclusion of AI modules for the training of the radiographer of the future has been suggested. Recommendations for development of AI strategies by national societies and regulatory bodies will harmonise the implementation efforts. Finally, there is need for collaboration between clinical practitioners and academia to ensure that the future radiography workforce is well prepared for the AI-enabled clinical environment.
https://eprints.bournemouth.ac.uk/35911/
Source: Scopus
Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting.
Authors: Wuni, A.-R., Botwe, B.O. and Akudjedu, T.N.
Journal: Radiography (Lond)
Volume: 27 Suppl 1
Pages: S69-S73
eISSN: 1532-2831
DOI: 10.1016/j.radi.2021.07.021
Abstract:OBJECTIVES: Current trends in clinical radiography practice include the integration of artificial intelligence (AI) and related applications to improve patient care and enhance research. However, in low resource countries there are unique barriers to the process of AI integration. Using Ghana as a case study, this paper seeks to discuss the potential impact of AI on future radiographic practice in low-resource settings. The opportunities, challenges and the way forward to optimise the potential benefits of AI in future practice within these settings have been explored. KEY FINDINGS: Some of the barriers to AI integration into radiographic practice relate to lack of regulatory and legal policy frameworks and limited resource availability including unreliable internet connectivity and low expert skillset. CONCLUSION: These barriers notwithstanding, AI presents a great potential to the growth of medical imaging and subsequently improving quality of healthcare delivery in the near future. For example, AI-enabled radiographer reporting has a potential to improving quality of healthcare, especially in low-resource settings like Ghana with an acute shortage of radiologists. In addition, futuristic AI-enabled advancements such as synthetic cross-modality transfer where images from one modality are used as a baseline to generate a corresponding image of another modality without the need for additional scanning will be of particular benefit in low-resource settings. IMPLICATIONS FOR PRACTICE: The urgent need for inclusion of AI modules for the training of the radiographer of the future has been suggested. Recommendations for development of AI strategies by national societies and regulatory bodies will harmonise the implementation efforts. Finally, there is need for collaboration between clinical practitioners and academia to ensure that the future radiography workforce is well prepared for the AI-enabled clinical environment.
https://eprints.bournemouth.ac.uk/35911/
Source: PubMed
Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting
Authors: Wuni, A.-R., Botwe, B.O. and Akudjedu, T.N.
Journal: RADIOGRAPHY
Volume: 27
Issue: S1
Pages: S69-S73
eISSN: 1532-2831
ISSN: 1078-8174
DOI: 10.1016/j.radi.2021.07.021
https://eprints.bournemouth.ac.uk/35911/
Source: Web of Science (Lite)
Impact of artificial intelligence on clinical radiography practice: futuristic prospects in a low resource setting
Authors: Abdul-Razak, W., Botwe, O.B. and Akudjedu, T.N.
Journal: Radiography
Publisher: Elsevier
ISSN: 1078-8174
https://eprints.bournemouth.ac.uk/35911/
Source: Manual
Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting.
Authors: Wuni, A.-R., Botwe, B.O. and Akudjedu, T.N.
Journal: Radiography (London, England : 1995)
Volume: 27 Suppl 1
Pages: S69-S73
eISSN: 1532-2831
ISSN: 1078-8174
DOI: 10.1016/j.radi.2021.07.021
Abstract:Objectives
Current trends in clinical radiography practice include the integration of artificial intelligence (AI) and related applications to improve patient care and enhance research. However, in low resource countries there are unique barriers to the process of AI integration. Using Ghana as a case study, this paper seeks to discuss the potential impact of AI on future radiographic practice in low-resource settings. The opportunities, challenges and the way forward to optimise the potential benefits of AI in future practice within these settings have been explored.Key findings
Some of the barriers to AI integration into radiographic practice relate to lack of regulatory and legal policy frameworks and limited resource availability including unreliable internet connectivity and low expert skillset.Conclusion
These barriers notwithstanding, AI presents a great potential to the growth of medical imaging and subsequently improving quality of healthcare delivery in the near future. For example, AI-enabled radiographer reporting has a potential to improving quality of healthcare, especially in low-resource settings like Ghana with an acute shortage of radiologists. In addition, futuristic AI-enabled advancements such as synthetic cross-modality transfer where images from one modality are used as a baseline to generate a corresponding image of another modality without the need for additional scanning will be of particular benefit in low-resource settings.Implications for practice
The urgent need for inclusion of AI modules for the training of the radiographer of the future has been suggested. Recommendations for development of AI strategies by national societies and regulatory bodies will harmonise the implementation efforts. Finally, there is need for collaboration between clinical practitioners and academia to ensure that the future radiography workforce is well prepared for the AI-enabled clinical environment.https://eprints.bournemouth.ac.uk/35911/
Source: Europe PubMed Central
Impact of artificial intelligence on clinical radiography practice: futuristic prospects in a low resource setting
Authors: Abdul-Razak, W., Botwe, O.B. and Akudjedu, T.N.
Journal: Radiography
Volume: 27
Issue: S1
Pages: S69-S73
ISSN: 1078-8174
Abstract:Current trends in clinical radiography practice include the integration of artificial intelligence (AI) and related applications to improve patient care and enhance research. However, in low resource countries there are unique barriers to the process of AI integration. Using Ghana as a case study, this paper seeks to discuss the potential impact of AI on future radiographic practice in low-resource settings. The opportunities, challenges and the way forward to optimise the potential benefits of AI in future practice within these settings have been explored.
Some of the barriers to AI integration into radiographic practice relate to lack of regulatory and legal policy frameworks and limited resource availability including unreliable internet connectivity and low expert skillset.
These barriers notwithstanding, AI presents a great potential to the growth of medical imaging and subsequently improving quality of healthcare delivery in the near future. For example, AI-enabled radiographer reporting has a potential to improving quality of healthcare, especially in low-resource settings like Ghana with an acute shortage of radiologists. In addition, futuristic AI-enabled advancements such as synthetic crossmodality transfer where images from one modality are used as a baseline to generate a corresponding image of another modality without the need for additional scanning will be of particular benefit in low-resource settings.
The urgent need for inclusion of AI modules for the training of the radiographer of the future has been suggested. Recommendations for development of AI strategies by national societies and regulatory bodies will harmonise the implementation efforts. Finally, there is need for collaboration between clinical practitioners and academia to ensure that the future radiography workforce is well prepared for the AI-enabled clinical environment.
https://eprints.bournemouth.ac.uk/35911/
Source: BURO EPrints