Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers' perspectives

被引:28
作者
Antwi, William Kwadwo [1 ]
Akudjedu, Theophilus N. [2 ]
Botwe, Benard Ohene [1 ]
机构
[1] Univ Ghana, Sch Biomed & Allied Hlth Sci, Dept Radiog, Coll Hlth Sci, Box KB143, Accra, Ghana
[2] Bournemouth Univ, Inst Med Imaging & Visualisat, Fac Hlth & Social Sci, Dept Med Sci & Publ Hlth, Bournemouth, Dorset, England
关键词
Artificial intelligence; Medical imaging; Radiography; Africa; Online surveys; Qualitative study; MANAGEMENT;
D O I
10.1186/s13244-021-01028-z
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose Studies have documented the clinical potentials of artificial intelligence (AI) in medical imaging practice to improving patient care. This study aimed to qualitatively explore the perception of radiographers relating to the integration of AI in medical imaging practice in Africa. Methods The study employed a qualitative design using an open-ended online instrument administered between March and August 2020. Participants consisted of radiographers working within Africa during the time of the study. Data obtained were analysed using qualitative content analysis. Six themes of concerns were generated: expectant tool; career insecurity; cost of new technology, equipment preservation and data insecurity; service delivery quality; need for expanding AI awareness. Results A total of 475 valid responses were obtained. Participants demonstrated a positive outlook about AI in relation to clinical quality improvement, competent diagnosis, radiation dose reduction and improvement in research. They however expressed concerns relating to the implementation of this technology, including job security and loss of core professional radiographer skills and roles. In addition, concerns regarding AI equipment maintenance, lack of awareness about AI and education and training opportunities were evident. Conclusion Awareness of the importance of AI in medical imaging practice was acknowledged; however, concerns relating to job security, data protection must be given critical attention for successful implementation of these advanced technologies in medical imaging in Africa. Inclusion of AI modules in the training of future radiographers is highly recommended.
引用
收藏
页数:9
相关论文
共 30 条
[1]   Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice [J].
Abuzaid, Mohamed M. ;
Elshami, Wiam ;
Tekin, Huseyin ;
Issa, Bashar .
ACADEMIC RADIOLOGY, 2022, 29 (01) :87-94
[2]   Overview of artificial intelligence in medicine [J].
Amisha ;
Malik, Paras ;
Pathania, Monika ;
Rathaur, Vyas Kumar .
JOURNAL OF FAMILY MEDICINE AND PRIMARY CARE, 2019, 8 (07) :2328-2331
[3]  
[Anonymous], QUAL HEALTH RES
[4]   An Investigation into the Infrastructure and Management of Computerized Tomography Units in Ghana [J].
Botwe, Benard ;
Schandorf, Cyril ;
Inkoom, Stephen ;
Faanu, Augustine .
JOURNAL OF MEDICAL IMAGING AND RADIATION SCIENCES, 2020, 51 (01) :165-172
[5]  
Botwe B, 2020, RADIOL TECHNOL, V91, P324
[6]   Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study [J].
Botwe, Benard O. ;
Antwi, William K. ;
Arkoh, Samuel ;
Akudjedu, Theophilus N. .
JOURNAL OF MEDICAL RADIATION SCIENCES, 2021, 68 (03) :260-268
[7]  
Botwe OB, 2021, RADIOGRAPHY, V20
[8]   When Chatbots Meet Patients: One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot [J].
Chaix, Benjamin ;
Bibault, Jean-Emmanuel ;
Pienkowski, Arthur ;
Delamon, Guillaume ;
Guillemasse, Arthur ;
Nectoux, Pierre ;
Brouard, Benoit .
JMIR CANCER, 2019, 5 (01)
[9]   Artificial Intelligence in Medical Applications [J].
Chan, Yung-Kuan ;
Chen, Yung-Fu ;
Pham, Tuan ;
Chang, Weide ;
Hsieh, Ming-Yuan .
JOURNAL OF HEALTHCARE ENGINEERING, 2018, 2018
[10]  
Davenport Thomas, 2019, Future Healthc J, V6, P94, DOI 10.7861/futurehosp.6-2-94