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. 2023 Oct 3;96(1152):20221157. doi: 10.1259/bjr.20221157

Table 2.

Data extraction form

Title Author(s) Year Country Aim/purpose Population/
sample		 Methods Intervention type Duration of intervention Key findings related to the scoping review question
A governance model for the application of AI in health Care. Reddy et al. 2020 Australia To propose an AI governance model. n/a Narrative review n/a n/a

  • Key ethical challenges (privacy, patient/clinician trust).

  • Algorithm biases.

  • Regulatory concerns.

  • Fairness, transparency, trustworthiness, accountability.

  • Liability issues.

  • XAI.
A guide to good practice for digital and data-driven health technologies. Department of Health & Social Care 2021 UK To support innovators in understanding what the NHS is looking for when it buys digital and data-driven technology for use in health and care. n/a Grey literature n/a n/a

  • Ethics.

  • Understanding users’ needs.

  • Ensure usability and accessibility.

  • Validation testing.

  • Ensure clinical safety.

  • Data protection.

  • Fairness, transparency.

  • Cybersecurity.

  • Algorithmic biases.

  • Regulation (UK).

  • Interoperability.

  • Data standards.
Evidence standards framework (ESF) for digital health technologies. National Institute for Health and Care Excellence. 2022 UK For companies that develop or distribute, and for evaluators and decision makers in the health and care system. n/a Grey literature n/a n/a

  • Classification of DHTs by intended purpose.

  • Design factors (safety, technical standards, reliability).

  • Intended purpose and target population.

  • Expected cost, health, and resource impacts.

  • Ensure DHTs effectiveness.

  • Evaluate changes over time.

  • Cost-effectiveness analysis.

  • Ensure transparency during deployment.

  • Ensure appropriate scalability.

  • Communication and education for end users.
Ethics Guidelines for trustworthy AI. European Commission 2019 Belgium To promote trustworthy AI and set out a framework to achieve this. n/a Grey literature n/a n/a

  • Ethics (human autonomy, fairness, no harm, explicability, attention to vulnerable groups).

  • 7 key requirements for trustworthy AI (human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination and fairness, environmental and societal well-being, and accountability.

  • Effectively communicate information to stakeholders.

  • Train new experts on AI.

  • Foster research and innovation.

  • Adopt a trustworthy AI assessment list when developing, deploying, or using AI systems, and adapt it to the specific use case in which the system is being applied.
Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research. Mirbadaie et al. 2021 Germany To identify the status quo of interdisciplinary research in academia on ethical
considerations and dimensions of AI in hospitals.		 15 articles published in medical journals. Systematic discourse n/a n/a

  • Transparency

  • Trustworthiness

  • Fairness

  • Liability

  • Accountability

  • Explainability

  • Education of workforce

  • Patient safety

  • Privacy

  • Informed consent

  • Security

  • Interoperability

  • Beneficence, justice, autonomy, non-maleficence.
Artificial Intelligence and Healthcare Regulatory and
Legal Concerns.		 Ganapathy K. 2021 India To discuss liability issues when AI is deployed in healthcare. n/a Narrative Review n/a n/a

  • XAI.

  • Data protection.

  • Trustworthy AI.

  • Informed consent.

  • Regulation (IMDRF and FDA).

  • Liability concerns.
Artificial Intelligence and Liability
in Medicine: Balancing Safety and Innovation.		 Maliha et al. 2021 USA To discuss liability issues. n/a Narrative Review n/a n/a

  • Regulation (FDA).

  • Liability issues (negligence, medical malpractice, product liability).

  • Legislation (USA).
Artificial intelligence and medical imaging
2018: French Radiology Community white
Paper.		 SFR-IA Group, CERF 2018 France To issue a position paper on AI. n/a Narrative Review n/a n/a

  • Regulation (GDPR, French legislation).

  • Data protection, pseudonymization.

  • Research (France).

  • Education (radiologists only).

  • Algorithm biases.

  • Ethics.
Artificial intelligence as a medical device in radiology: ethical
and regulatory issues in Europe and the United States.		 Pesapane et al. 2018 Binational To analyse the legal framework
regulating medical devices and data protection in Europe and in the United States.		 n/a Narrative Review n/a n/a

  • Regulation (EU and USA).

  • Data protection (EU and USA).

  • Accountability.
Artificial Intelligence in Cardiovascular Imaging:
“Unexplainable” Legal and Ethical Challenges?		 Lang et al. 2022 Canada To discuss legal and ethical issues arising from unexplainable AI models. n/a Narrative Review n/a n/a

  • XAI

  • Regulation (Canada and USA).

  • Liability.

  • Data protection (EU).
Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. Schonberger D. 2019 UK To discuss ethical and legal challenges of AI in healthcare. n/a Narrative review n/a n/a

  • Regulation (UK, EU, USA).

  • Fairness.

  • Non-discrimination laws (UK and EU).

  • Respect for autonomy laws (UK and EU).

  • Informed consent (UK).

  • GDPR

  • Accountability laws (EU).

  • Liability (UK).

  • Transparency.
Artificial Intelligence in Radiology—
Ethical Considerations.		 Brady & Neri 2020 Multinational To explain some of the ethical challenges, and some of the measures we may take to protect against misuse of AI. n/a Narrative review n/a n/a

  • Data ownership and privacy.

  • GDPR.

  • Informed consent.

  • Data anonymization.

  • Data biases

  • Transparency

  • Interpretability

  • Explainability

  • Resource inequality.

  • Liability (EU)

  • Explicability
Canadian Association of Radiologists White Paper
on Artificial Intelligence in Radiology.		 Tang et al. 2018 Binational To inform CAR members and policymakers on key terminology, educational needs of members, research and development,
partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.		 n/a Narrative Review n/a n/a

  • Education

  • Data anonymization.

  • Data sharing

  • Data biases

  • Interoperability

  • Multidisciplinary teams engaged.
Checklist for Artificial Intelligence in Medical Imaging
(CLAIM): A Guide for Authors and Reviewers.		 Mongan et al. 2020 USA To propose CLAIM, the Checklist for AI in
Medical Imaging.		 n/a Narrative Review n/a n/a -Guidelines for reporting AI studies.
DECIDE-AI: new reporting guidelines to bridge the development to implementation gap in clinical artificial intelligence. The DECIDE-AI Steering Group. 2021 Multinational To propose guidelines to report key information items between the in silico algorithm development/validation and large-scale clinical trials evaluating AI interventions. n/a Narrative review n/a n/a -Reporting guidelines.
Do no harm: a roadmap for responsible machine learning for health care. Wiens et al. 2019 Binational To provide a comprehensive overview of the barriers to deployment and translational impact. n/a Narrative review n/a n/a

  • Ethics

  • Algorithm biases

  • Data biases

  • Model evaluation

  • Validation

  • Reporting guidelines.

  • Regulation (USA)
Emerging Consensus on ‘Ethical AI’: Human
Rights Critique of Stakeholder Guidelines.		 Fukuda-Parr & Gibbons 2021 USA To review 15 guidelines preselected to be strongest on human rights, and on global health. 15 guidelines Narrative review n/a n/a

  • Respect to privacy and freedom of expression.

  • Human rights laws (international).

  • Universality

  • Accountability

  • Equality

  • Participation

  • Informed consent

  • Regulation (EU, USA, Canada).
Ethical and legal challenges of informed consent applying artificial intelligence in medical diagnostic consultations. Astromske et al. 2021 Lithuania To discuss the process of informed consent when using AI. n/a Narrative review n/a n/a

  • Informed consent

  • Trustworthiness

  • Informed consent legislation (EU and USA).

  • GDPR
Ethical considerations for artificial intelligence: an overview of the current radiology landscape. D'Antonoli TA 2020 Switzerland To discuss ethical issues around AI. n/a Narrative review n/a n/a

  • Algorithm biases

  • Transparency

  • Accountability

  • Multidisciplinary stakeholders involved.

  • Regulation (EU, USA, Canada).

  • Data privacy

  • Education

  • Liability

  • Explicability

  • Autonomy
Ethics of Artificial Intelligence in Radiology: Summary of the
Joint European and North American Multisociety Statement.		 Geis et al. 2019 Multinational Summary of a joint statement on AI Ethics. n/a Narrative review n/a n/a

  • Transparency

  • Accountability

  • Informed consent

  • Data privacy

  • Data ownership

  • Algorithm biases

  • Cybersecurity

  • GDPR

  • Trustworthiness

  • Liability
Ethics of Using and Sharing Clinical Imaging Data for
Artificial Intelligence: A Proposed Framework.		 Larson et al. 2020 USA To propose an ethical framework for using and sharing clinical data for the development of AI applications. n/a Narrative review n/a n/a

  • Ethics

  • Privacy of data

  • Data protection

  • Legislation (USA)

  • Informed consent
Evaluation and Real-World
Performance Monitoring of Artificial
Intelligence Models in Clinical Practice:
Try It, Buy It, Check It.		 Allen et al. 2021 USA To discuss why regulatory clearance alone may not be enough to ensure AI will be safe and effective in all radiological practices. n/a Narrative review n/a n/a

  • Validation

  • Model evaluation

  • Regulation (USA)

  • Performance monitoring.
FUTURE-AI: Guiding Principles and Consensus
Recommendations for Trustworthy Artificial Intelligence in
Medical Imaging.		 Lekadir et al. 2021 Multinational To introduce a careful selection of guiding principles drawn from the accumulated
experiences, consensus, and best practices from five large European projects on AI in Health
Imaging.		 n/a Narrative review n/a n/a

  • Fairness

  • Universality

  • Traceability

  • Usability

  • Robustness

  • Explainability
Identifying Ethical Considerations for Machine
Learning Healthcare Applications.		 Char et al. 2020 USA To identify ethical concerns of ML healthcare applications. n/a Narrative review n/a n/a

  • Transparency

  • Accountability

  • Informed consent

  • Justice

  • Auditability

  • Diverse stakeholders.

  • Data biases
A Buyer’s Guide to
AI in Health and Care.		 NHSx 2020 UK To offer practical guidance on the questions to be asking before and
during any AI procurement exercise in health and care.		 n/a Grey literature n/a n/a

  • Regulation (UK)

  • Validation

  • Model evaluation

  • Ethics

  • Data protection

  • Fairness

  • Transparency
Privacy in the age of medical big data. Price & Cohen 2019 Binational To discuss patient privacy, consent, and data collection. n/a Narrative review n/a n/a

  • Data privacy

  • Informed consent

  • Data sharing
Regulatory Frameworks for
Development and Evaluation of
Artificial Intelligence–Based Diagnostic
Imaging Algorithms: Summary and
Recommendations.		 Larson et al. 2021 Binational To review the major regulatory frameworks for software as a medical device applications, identify major gaps, and propose additional strategies to improve the development and
evaluation of diagnostic AI algorithms.		 n/a Narrative review n/a n/a

  • Regulation (EU, USA, International).

  • Validation

  • Model testing in all relevant domains.

  • Continuous monitoring of performance.

  • Algorithm durability.

  • Assessment by third-party evaluators.
Reporting guidelines for clinical trials of
artificial intelligence interventions: the SPIR
IT-AI and CONSORT-AI guidelines.		 Ibrahim et al. 2021 Binational. To build new guidelines to report clinical trials of AI interventions. n/a Narrative review n/a n/a -Reporting guidelines
The European artificial intelligence strategy: implications and challenges for digital health. Cohen et al. 2020 Multinational To present the challenges associated with the European Commission white paper on AI. n/a Narrative review n/a n/a

  • GDPR

  • Regulation (EU and USA).

  • Data privacy

  • Algorithm update problems.

  • Fairness

  • Accountability
The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence. Cabitza & Zeitoun 2019 Binational To propose four types of validity corresponding to different perspectives to evaluate true clinical validity. n/a Narrative review n/a n/a

  • Model validation

  • Statistical validity, relational validity, pragmatic validity, ecological validity.
The roadmap to an effective AI assurance ecosystem. Centre for Data Ethics and Innovation 2021 UK To set out the steps needed to grow a mature AI assurance industry. n/a Grey literature n/a n/a

  • Trustworthiness

  • Assessment

  • Cybersecurity (UK).

  • Regulation (UK)

  • Performance testing.
To buy or not to buy—evaluating commercial AI solutions
in radiology (the ECLAIR guidelines).		 Omoumi et al. 2021 Multinational To propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology and reach an informed decision. n/a Narrative review n/a n/a

  • Model performance.

  • Validation

  • Usability

  • Interoperability

  • Interpretability

  • Regulation (UK, EU, USA).

  • Financial issues
Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis. Morley et al. 2021 UK To set out a minimally viable framework for evaluating the safety, acceptability and efficacy of AI systems for healthcare. n/a Narrative review n/a n/a

  • Transparency

  • Validation

  • Clinical efficacy tests.

  • Ongoing monitoring.

  • Robustness
Understanding healthcare workers’ confidence in AI. NHS AI Lab & Health Education England 2022 UK To explore the factors
influencing healthcare workers’ confidence in AI technologies and how these can inform the development of related education and training.		 n/a Grey literature n/a n/a

  • Regulation (UK)

  • Evaluation

  • Validation

  • Trustworthiness

  • Confidence

  • Liability
Digital Technology Assessment Criteria (DTAC) NHS England 2021 UK To give patients and staff the confidence that the digital tools they use meet NHS’ clinical safety, data protection, technical security, interoperability and usability and accessibility standards. n/a Grey literature n/a n/a

  • Ensure clinical safety.

  • Data protection

  • Cybersecurity

  • Interoperability criteria.

  • Usability