Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology

Mollura, Daniel J.; Culp, Melissa P.; Pollack, Erica; Battino, Gillian; Scheel, John R.; Mango, Victoria L.; Elahi, Ameena; Schweitzer, Alan; Dako, Farouk

doi:10.1148/radiol.2020201434

Cited by 103 publications

(64 citation statements)

References 19 publications

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“…1,7,9,15 The fact that AI-related decision support systems can accurately produce diagnostic results through triaging and flagging of abnormal images of patients 3,6 suggests that its integration in medical imaging is improving practice and has the capacity to help more patients without access to prompt radiological interpretation, like the rural parts of Ghana and other resource poor regions of the world. 8 This is believed to increase the levels of accuracy in diagnosing diseases in a short time and improve decisionmaking on diagnostic results of patients and quality assurance in many aspects of radiography practice. In academia, AI tools are also thought to improve education in medical imaging and promote research productivity in radiology which supports the findings of Sarwar et al 14 MS + = mean score out of an aggregated total of 5 on the positive impact of AI in medical imaging.…”

Section: Discussionmentioning

confidence: 99%

“…5 Although image interpretation is possibly the most well-researched task of AI in medical imaging in an attempt to improve the detection of pathologies 3,4,6 , current studies are focussed on its application beyond this scope to broadly support imaging professionals in achieving optimal results with ease. 1,[7][8][9][10][11] Particularly, AI tools are being used as clinical decision support enhancers and supportive systems for improving imaging workflow, image acquisition, disease identification, research efficiency, radiation exposures and delivering high-quality care. 1,6,9 A recent meta-analysis demonstrated that the diagnostic performance of these technologies is equivalent to that of healthcare professionals.…”

Section: Introductionmentioning

confidence: 99%

“…3 Despite the aforementioned benefits, scarcity of technical expertise, data-right frameworks, public policies and latest physical resources have impeded the adoption of AI in medical imaging in Ghana and other low-and middle-income countries. 8 Notwithstanding, there are strong attempts by Governments and other nongovernmental organisations (e.g. RAD-AID) to promote and integrate the use of AI technologies in medical imaging in relatively low resource environments.…”

Section: Introductionmentioning

confidence: 99%

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Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

Botwe

Antwi

Arkoh

et al. 2021

J of Medical Radiation Sci

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Introduction: The integration of artificial intelligence (AI) systems into medical imaging is advancing the practice and patient care. It is thought to further revolutionise the entire field in the near future. This study explored Ghanaian radiographers' perspectives on the integration of AI into medical imaging. Methods: A cross-sectional online survey of registered Ghanaian radiographers was conducted within a 3-month period (February-April, 2020). The survey sought information relating to demography, general perspectives on AI and implementation issues. Descriptive and inferential statistics were used for data analyses. Results: A response rate of 64.5% (151/234) was achieved. Majority of the respondents (n = 122, 80.8%) agreed that AI technology is the future of medical imaging. A good number of them (n = 131, 87.4%) indicated that AI would have an overall positive impact on medical imaging practice. However, some expressed fears about AI-related errors (n = 126, 83.4%), while others expressed concerns relating to job security (n = 35, 23.2%). High equipment cost, lack of knowledge and fear of cyber threats were identified as some factors hindering AI implementation in Ghana. Conclusions: The radiographers who responded to this survey demonstrated a positive attitude towards the integration of AI into medical imaging. However, there were concerns about AI-related errors, job displacement and salary reduction which need to be addressed. Lack of knowledge, high equipment cost and cyber threats could impede the implementation of AI in medical imaging in Ghana. These findings are likely comparable to most low resource countries and we suggest more education to promote credibility of AI in practice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

Botwe

Antwi

Arkoh

et al. 2021

J of Medical Radiation Sci

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“…AI already has the potential to improve the workflow in radiology, and in the future might help in interpretation by automated detection of abnormalities in chest, brain and other body regions, which might have considerable impact in LMICs [25]. Some vendors already use AI installed in the equipment, and many AI software solutions are open source.…”

Section: Technical Solutionsmentioning

confidence: 99%

How to improve access to medical imaging in low- and middle-income countries ?

et al. 2021

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Imaging has become key in the care pathway of communicable and non-communicable diseases. Yet, there are major shortages of imaging equipment and workforce in low-and middle-income countries (LMICs). The International Society of Radiology outlines a plan to upscale the role of imaging in the global health agenda and proposes a holistic approach for LMICs. A generic model for organising imaging services in LMICs via regional Centres of Reference is presented. The need to better exploit IT and the potential of artificial intelligence for imaging, also in the LMIC setting, is highlighted. To implement the proposed plan, involvement of professional and international organisations is considered crucial. The establishment of an International Commission on Medical Imaging under the umbrella of international organisations is suggested and collaboration with other diagnostic disciplines is encouraged to raise awareness of the importance to upscale diagnostics at large and to foster its integration into the care pathway globally.

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“…The research and development and eventual adoption of AI for medical decision making in global health and low-resource settings are hampered by insufficient infrastructure (Mollura et al, 2020).However, it is essential that local radiology and clinical community, resource-poor or not, have to develop and validate AI tools suitable for their environment. In the resourcepoor regions with limited infrastructure, technical and human, such participation could be difficult.…”

Section: Research and Development Environment In Resource-limited Regionsmentioning

confidence: 99%

Artificial Intelligence for the Future Radiology Diagnostic Service

Mun

Wong

et al. 2021

Front. Mol. Biosci.

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Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.

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Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology

Cited by 103 publications

References 19 publications

Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

How to improve access to medical imaging in low- and middle-income countries ?

Artificial Intelligence for the Future Radiology Diagnostic Service

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