Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 13, 2024
Date Accepted: Nov 28, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Challenges and opportunities for data sharing related to Artificial Intelligence AI tools in healthcare in low- and middle-income countries: A systematic review and case study from Thailand.
ABSTRACT
Background:
Low- and middle-income countries (LMICs) face significant challenges in sharing data for developing and deploying artificial intelligence (AI) in healthcare.
Objective:
This study aims to identify barriers and enablers to data sharing for AI in healthcare in LMICs.
Methods:
First, we performed a systematic literature search using PubMed, SCOPUS, Embase, Web of Science, and ACM (Association for Computing Machinery) to identify barriers and enablers to share data for AI in LMICs. We classified these according to seven categories: 1) Technical, 2) Motivational, 3) Economic, 4) Political, Legal and Policy, 5) Ethical, 6) Social, and 7) Organisational and Managerial. Second, we tested the local relevance of barriers and enablers using a case study in Thailand through stakeholder interviews with 15 academic experts, technology developers, regulators, policymakers, and healthcare providers.
Results:
We identified 22 studies, mostly from Africa (n=12, 55%) and Asia (n=6, 27%). The most important data-sharing challenges were unreliable internet connectivity, lack of equipment, poor staff and management motivation, uneven resource distribution, and ethical concerns. Possible solutions included improving IT infrastructure, enhancing funding, introducing user-friendly software, and incentivising healthcare organisations and personnel to share data for AI-related tools. In Thailand, inconsistent data systems, limited staff time, low health data literacy, complex and unclear policies and cybersecurity issues were important data-sharing challenges. Solutions included improving and clarifying current policies and introducing interoperable and standardised data formats.
Conclusions:
LMICs face significant technical, political, legal, policy, and organisational barriers to sharing data for AI, which significantly impede localised AI development and deployment. The results of this study can be adapted to local contexts to guide policy formulation and recommendations. Clinical Trial: NA.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.