The study identifies a gap in the existing literature

The gap

Evidence profile

Sourced from the recommendations and limitations and stated research gap of the source papers, classified as general, spanning 3 journals. Those papers have been cited 1 times in total.

Supporting evidence — 3 representative gaps

• Perspectives on healthcare artificial intelligence policy from health equity professionals: findings from an interview study (2026) · Frontiers in Digital Health · doi

  Theme 1: Developing health AI policy 1. Increase community diversity and representation in datasets, add “warning label” to datasets which lack diversity for health equity starts with data 2. Learn about how social contexts and biases are embedded in health data 3. Identify power asymmetries in data collection and build structures for community access to health data for AI 4. Encourage collection and inclusion of data that gives a fuller picture of health and life history, such as data from caregivers Theme 2: Health AI policy for health 1. Longer term commitments from funders for AI and health disparities research, also include small research institutes; equity must include multiple encourage academic and community research partnerships to investigate health equity in AI institutions and strategies 2. Encourage federal agencies to investigate how health AI tools may violate rights 3. Include robust evidence on AI benefit and equity as part of regulating health AI, such as clinical trials as part of FDA AI regulation 4. Encourage accreditors such as Joint Commissions to include health equity in assessment of health AI Theme 3: Considering economic issues 1. Use AI tools to identify high-needs patients without expecting organizations to “do more with less” is key for developing health AI policy 2. Pitch equitable AI as a marketing strategy that advances health equity 3. Invest in under-resourced healthcare facilities to increase capacity to use and maintain AI solutions 4. Consider how integration of health AI tools can have workforce implications getting health data can be an “extractive” process, and that measures should be taken to ensure that there is mutual benefit: tools. The the data collection “communities who want the data, who are generating the data, and that either they collect about themselves or that others collect about them, you have large institutions that, you know, deploy large organizations benefit from the use of that data, but again they’re larger organizations that have a lot more power in the communities too, so, for one, recognizing that the communities have access to the data that’s being collected about them. Access such that they don’t need to overcome paywalls or overcome significant technical boundaries that might exist around them accessing data collected about them … Do the pipelines exist for communities to benefit from that data in a way that empowers and enriches? In [a way] that helps communities address, you know, the needs that they have right then and now … organizations that collect data or extract data from and about communities. What are they responsible for?

  generalrecommendationsevidence 5/5
  Keywords: health equity communities them encourage include tools bene organizations theme policy community collection access collect
• Cloud, control and diagnostic sovereignty: the political economy of AI-enabled health diagnostics in Africa (2026) · Journal of Global Health Economics and Policy · cited 1× · doi

  Several limitations must be acknowledged. First, this study relies exclusively on publicly available secondary data, including firm websites, published academic literature, public policy documents, and accessible regulatory instruments. No interviews were conducted with firm representatives, regulators, clinicians, patients, or cloud providers. The study therefore cannot independently verify internal infrastructure configurations, contractual arrangements, retraining practices, or compliance procedures. Second, the purposive selection of four firms from three countries prioritises analytical relevance over representativeness. Firms with strong public visibility and links to foreign cloud infrastructure may be overrepresented relative to smaller, community-based, locally financed, or publicsector initiatives. The findings should therefore not be generalised to all African health AI projects. Journal of Global Health Economics and Policy 5 Cloud, control and diagnostic sovereignty: the political economy of AI-enabled health diagnostics in Africa Table 3.

  generallimitationsevidence 5/5
  Keywords: public policy cloud health firm firms enabled diagnostics africa several limitations must acknowledged first relies
• Artificial Intelligence (AI) and Healthcare Capabilities: A Systematic Review and Research Directions (2026) · F1000Research · doi

  The study identifies a gap in the existing literature on AI and healthcare, highlighting the need for a systematic review of the current state of research in the field. The study notes that while AI has the potential to revolutionize the healthcare ecosystem, there is a lack of understanding of how healthcare organizations can leverage AI-enabled capabilities to create organizational value.

  generalstated research gapevidence 5/5
  Keywords: study identifies gap existing literature healthcare highlighting need

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