Table 1.
Characteristics of included articles
| Study ID | Title (year) | Article type | Description of record | Access | Type of collaboration |
|---|---|---|---|---|---|
| SR1 | Heterogeneity/granularity in ethnicity classifications project: the need for refining assessment of health status (2018)46 | Journal article | Description of how ethnicity is recorded across different EU countries; some collect highly granular data, some allow free text expression, others allow only limited categories | Open access | International collaboration |
| SR2 | Bringing the people back In: contesting benchmark machine learning datasets (2020)47 | Preprint | Outlines the concept of benchmark datasets as a form of research infrastructure and key factors that may influence a dataset’s value and utility | Open access | National collaboration |
| SR3 | A framework for understanding sources of harm throughout the machine learning life cycle (2019)24 | Preprint | Maps where biases may cause harm during a ML development pipeline | Open access | Single institution |
| SR4 | Datasheets for datasets (2018)32 | Preprint | Introduces a ‘Datasheet’ artifact, allowing dataset curators to provide a comprehensive, structured and standardized description of a dataset’s composition and the context in which it has been curated | Open access | National collaboration |
| SR5 | The dataset nutrition label: a framework to drive higher data quality standards (2018)48 | Preprint | Introduces a ‘Nutrition label’ artifact, allowing dataset curators to provide a structured, standardized summary of a dataset’s composition | Open access | National collaboration |
| SR6 | Ensuring that biomedical AI benefits diverse populations (2021)11 | Journal article | Highlights how AI development can cause biases and health disparity. Also indicates both short-term and longer-term solutions to mitigate some of these factors | Open access | Single institution |
| SR7 | How to design AI for social good: seven essential factors (2020)49 | Journal article | Identifies and explains seven essential ethical factors to consider when developing AI for social good. Each factor is followed by a recommendation for developers who are seeking to develop AI that promotes social good | Open access | National collaboration |
| SR8 | Identifying ethical considerations for machine learning healthcare applications (2020)50 | Journal article | Framework linking the ML development pipeline to evaluation and oversight of these technologies, highlighting where along this joint pathway ethical considerations and value-based issues may arise | Closed access | National collaboration |
| SR9 | Indigenous and tribal peoples data governance in health research: a systematic review (2021)51 | Journal article | Systematic review of data governance frameworks, processes, policies and practices for indigenous and tribal peoples | Open access | Single institution |
| SR10 | MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care (2020)52 | Journal article | Minimum reporting standards for studies of medical AI, relating to the study population and setting, patient demographic characteristics, model architecture and model evaluation | Open access | Single institution |
| SR11 | Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities (2020)53 | Journal article | Ethical discussion about the differences between algorithmic fairness and bias and a summary of different definitions of fairness | Open access | Single institution |
| SR12 | The reporting of race and ethnicity in medical and science journals: comments invited (2021)26 | Journal article | Guidance for reporting ethnicity and race in research articles specifically for JAMA Network journals | Open access | Single institution |
| SR13 | Ethical limitations of algorithmic fairness solutions in health care machine learning (2020)54 | Journal article | Commentary on how framing algorithmic fairness as entirely a technical problem can contribute to or cause health inequity unless social factors are also considered | Open access | National collaboration |
| SR14 | Missed policy opportunities to advance health equity by recording demographic data in electronic health records (2015)55 | Journal article | Description of how different US bodies and organizations take different approaches to collecting demographic data, including using different categories, which limits crosslinking between data sources | Closed access | Single institution |
| SR15 | Clinical collabsheets: 53 questions to guide a clinical collaboration (2020)22 | Conference proceedings | A guide to collaborating between clinicians and computer scientists to develop models in interdisciplinary teams across eight development stages | Open access | Multidisciplinary international collaboration |
| SR16 | Ethical machine learning in healthcare (2021)5 | Journal article | Overview of the five key stages in the healthcare ML model development pipeline, overlaying points at which ethical issues may arise | Open access | International collaboration |
| SR17 | Addressing health disparities in the Food and Drug Administration’s artificial intelligence and machine learning regulatory framework (2020)23 | Journal article | Commentary about how health disparities might be considered by the FDA software as a medical-device regulatory framework, through integration of premarket review and good ML practices and postmarket real-world performance monitoring | Open access | Single institution |
| SR18 | Model cards for model reporting (2018)56 | Preprint | Introduces a ‘Model card’ artifact, encouraging transparent reporting of ML model performance characteristics | Open access | National collaboration |
| SR19 | Canada protocol: an ethical checklist for the use of artificial intelligence in suicide prevention and mental health (2019)57 | Preprint | An ethical checklist for the use of AI in mental health and suicide prevention, validated by two-round Delphi consultation. Note that a version of this record was subsequently published closed access in a journal57 | Open access | Single institution |
| SR20 | Aequitas: a bias and fairness audit toolkit. (2018)58 | Preprint | An open-source bias audit toolkit to allow ML developers, analysts and policymakers to assess AI systems for biased outputs | Open access | Single institution |
| SR21 | AI-assisted decision-making in healthcare: the application of an ethics framework for big data in health and research (2019)25 | Journal article | A discussion of key ethical issues involved with AI implementation in healthcare, with specific case study examples | Open access | National collaboration |
| SR22 | An ethics framework for big data in health and research (2019)59 | Journal article | A framework of values underpinning ethical design of AI in healthcare, developed by a working group with expert feedback | Open access | International collaboration |
| SR23 | Artificial intelligence for genomic medicine—a policy analysis (2020)60 | Conference proceedings | Practical recommendations for policymakers in the field of AI and genomic medicine, exploring the drivers behind the use of AI in genomics, current applications and limitations and challenges | Open access | Single institution |
| SR24 | Big data science: opportunities and challenges to address minority health and health disparities in the 21st century (2017)61 | Journal article | A discussion of how big data science can be used to address minority health issues and actively reduce health disparities by changing the types and mechanisms of electronic health-data capture and enabling studies into health disparities. Also provides a series of recommendations to achieve these aims | Open access | National collaboration |
| SR25 | Ensuring fairness in machine learning to advance health equity (2018)62 | Journal article | Describes how health disparities can be worsened by model design, data biases and interpretation by patients and clinicians. Recommends that proactive distributive justice be incorporated into models to ensure equality in patient outcomes, resource allocation and model performance | Open access | Single institution |
| SR26 | Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics and effectiveness (2020)19 | Journal article | Framework for interdisciplinary groups researching, generating or implementing ML models to determine a model’s potential to benefit patients. Focuses on transparency, replicability, ethics and effectiveness | Open access | Multidisciplinary international collaboration |
| SR27 | Do no harm: a roadmap for responsible machine learning for health care (2019)21 | Journal article | A set of principles promoting practices that enable acceleration of translation of ethical and effective ML models in healthcare, spanning problem selection, development, ethical considerations, evaluation and reporting, deployment and postmarket considerations | Open access | Multidisciplinary international collaboration |
| SR28 | Addressing fairness, bias and appropriate use of artificial intelligence and machine learning in global health (2021)63 | Journal article | A framework for those deploying ML algorithms in low- and middle-income countries, focusing on determining whether a model is appropriately matched to the local context and target population, identifying biased performance and considering implications for fairness | Closed access | Single institution |
| SR29 | Artificial intelligence, bias and clinical safety (2019)20 | Conference proceedings | Discussion of potential medical AI errors and biases and presentation of quality-control questions enabling critical appraisal of medical AI research and highlighting potential pitfalls for future researchers | Open access | Multidisciplinary international collaboration |
| SR30 | Healthsheet: development of a transparency artifact for health datasets (2022)33 | Journal article | Introduces a ‘Healthsheet’ artifact, allowing healthcare dataset curators to provide a comprehensive, structured and standardized description of a dataset’s composition and the context in which it has been curated. Related to the ‘Datasheet’ artifact, but adapted for healthcare datasets | Open access | Single institution |
Results of literature search, including sources found through journal database searches, preprint servers and reference lists.