Table 1

Areas of emphasis for ensuring machine learning for healthcare (MLHC) works for all

Area of emphasisRecommendations
Ensure MLHC is equitable by design
  • Develop pipelines for the promotion of diverse teams in all aspects of MLHC

  • Ensure the inclusion of data from a broad range of groups, in a broad range of contexts

  • Incorporate global partners to ensure health data science promotes global health equity.

Encourage public and open MLHC research
  • Fund both direct MLHC research and research into ethical aspects of MLHC

  • Harmonise ethical oversight between public and private research domains

Ensure adequate access to health information technology (IT) infrastructure
  • Ensure all are included in the datasets by funding health data gathering infrastructure in underserved communities

  • Develop MLHC products with an awareness of the broad range of health IT contexts for deployment

Ensure MLHC is clinically effective and impactful
  • Ensure the presence of multidisciplinary teams that represent both clinical and data science perspectives

  • Promote pathways for interdisciplinary training

  • Hold MLHC innovations to the same standards as other healthcare interventions, including requirements for prospective validation and clear demonstration of impact

Audit MLHC on ethical metrics
  • Mandate assessments of the performance of novel MLHC technology for impacts on marginalised and intersectional groups.

  • Record the data necessary to perform these audits in an ongoing fashion

Mandate transparency in data collection, analysis and usage
  • Build patient trust by ensuring that protocols for the collection, analysis and usage of data are transparent and open

Promote inclusive and interoperable data policy
  • Ensure the existence of clear and ethical methods for ensuring the sharing of data between different sources while protecting patient rights and privacy

  • Improve the standardisation of medical data generation and labelling across contexts

  • Ensure that global partners are included, so that interoperability barriers do not hinder inclusive global collaboration