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Licensed Unlicensed Requires Authentication Published by De Gruyter October 21, 2022

A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift

  • Abdul Nashirudeen Mumuni EMAIL logo , Francis Hasford , Nicholas Iniobong Udeme , Michael Oluwaseun Dada and Bamidele Omotayo Awojoyogbe
From the journal Physical Sciences Reviews
https://doi.org/10.1515/psr-2022-0121

Abstract

Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.

Keywords: artificial intelligence; computer aided diagnostics; developing countries; diagnostic imaging; machine learning; SWOT analysis
Corresponding author: Abdul Nashirudeen Mumuni, Department of Medical Imaging, University for Development Studies, Tamale, Ghana, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

Appendix

Summary of SWOT analysis of AI in diagnostic imaging in the developing world.

Strengths Weaknesses Opportunities Threats

  1. Enhanced diagnosis

  1. Need for big data

  1. Increased access to imaging services

  1. Input training data biases

  1. Disease monitoring and prediction

  1. Huge initial and maintenance costs

  1. Image evaluation and reporting

  1. Lack of accountability and regulatory frameworks

  1. Accurate tumor classification

  1. Inadequate technical expertise

  1. Synthetic modality transfer

  1. Perceived transfer of human intervention

  1. Efficient image acquisition

  1. Data ethical concerns

  1. Protocol optimization

  1. Standardization of operational language

  1. Image processing, segmentation and analysis

  1. Data security and accuracy

  1. Improved patient care

  1. Poor investment in infrastructure

  1. Communal acceptability

  1. Audit of AI-based systems

  1. Perceived job losses and redundancy

  1. Availability of support systems

  1. AI missing in diagnostic imaging training

  1. Security breaches

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Received: 2022-06-02
Accepted: 2022-09-10
Published Online: 2022-10-21

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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Volume 9 Issue 1
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