Table 1.

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

Area of emphasis	Recommendations
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