Good Machine Learning Practice for Medical Device Development: Guiding Principles

Good Machine Learning Practice for Medical Device Development: Guiding Principles
MHRA 27 October 2021

• Ten guiding principles that can inform the development of Good Machine Learning Practice (GMLP). These guiding principles will help promote safe, effective and high-quality medical devices that use artificial intelligence and machine learning (AI/ML).
• Press release: UK, USA and Canadian regulators identify 10 guiding principles to be addressed when medical devices use AI or machine learning software - GOV.UK (www.gov.uk)

Ethical considerations in the use of Machine Learning for research and statistics

Ethical considerations in the use of Machine Learning for research and statistics
UK Statistics Authority 26 October 2021

• This high-level guidance explores ethical considerations associated with the use of machine learning techniques for research and statistical purposes. This guidance is not exhaustive, but aims to assist and support analysts, researchers, data scientists, and statisticians navigating the ethical issues surrounding machine learning based projects. Links to further resources are provided if you would like to read about particular aspects in more detail.

Planning and Evaluating Remote Consultation Services: A New Conceptual Framework Incorporating Complexity and Practical Ethics

Planning and Evaluating Remote Consultation Services: A New Conceptual Framework Incorporating Complexity and Practical Ethics
Frontiers in Digital Health, 13 August 2021 | https://doi.org/10.3389/fdgth.2021.726095

• Empirical findings have shown that decisions about remote consultation are fraught with contradictions and tensions—for example, between demand management and patient choice—leading to both large- and small-scale ethical dilemmas for managers, support staff, and clinicians. A novel framework, Planning and Evaluating Remote Consultation Services (PERCS) has been developed which focuses attention on the organizational digital maturity and digital inclusion efforts. The authors also present a set of principles for informing its application in practice, including education of professionals and patients.

Ethics and governance of artificial intelligence for health

Ethics and governance of artificial intelligence for health
WHO 28 June 2021

• The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use.

Health information technology and digital innovation for national learning health and care systems

Health information technology and digital innovation for national learning health and care systems
The Lancet Digital Health June 2021 v3(6) p e383-e396 

• A discussion of the opportunities around the use of digital health technology to support policy and planning, public health, and personalisation of care. Innovations include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public–private partnerships. To address the ethical issues there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public–private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Ethics, Transparency and Accountability Framework for Automated Decision-Making

Ethics, Transparency and Accountability Framework for Automated Decision-Making
Cabinet Office, Central Digital & Data Office and the Office for Artificial Intelligence 13 May 2021

• A 7 point framework which will help government departments with the safe, sustainable and ethical use of automated or algorithmic decision-making systems.

Ethics-Based Auditing to Develop Trustworthy AI

Ethics-Based Auditing to Develop Trustworthy AI.
Minds & Machines (2021). https://doi.org/10.1007/s11023-021-09557-8. 19 February 2021

• This article considers auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics.

Abstract

Data Ethics Framework

Data Ethics Framework
Government Digital Service updated 16 September 2020

• The Data Ethics Framework is a set of principles to guide the design of appropriate data use in the public sector. It is aimed at anyone working with data in the public sector, including:
• data practitioners (for example statisticians, analysts and data scientists)
• policymakers
• operational staff
• people helping to produce data-informed insight.

The ethics of AI in health care: A mapping review

The ethics of AI in health care: A mapping review
Social Science & Medicine Volume 260, September 2020, 113172

• A mapping review of the literature concerning the ethics of artificial intelligence in health care which aims to summarise current debates and identify open questions for future research.
• Overview: Policymakers must start asking difficult questions on the ethics of AI in healthcare, Public Technology 9 September 2020

Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic

Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic
Journal of Medical Ethics 04 May 2020. doi: 10.1136/medethics-2020-106314

• A discussion of the ethical implications of the use of mobile phone apps in the control of the COVID-19 pandemic from The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK

How is the Centre for Data Ethics and Innovation supporting the response to COVID-19?

How is the CDEI supporting the response to COVID-19? [blog]
Centre for Data Ethics and Innovation 27 April 2020

• "The CDEI is supporting the public sector’s response ..., working to ensure that the speed at which innovation must move doesn’t demand that the values of transparency, privacy, scrutiny and good governance are foregone - compromising the public’s trust in public sector innovation longer term."

Ethics of Artificial Intelligence in Radiology

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement
Radiology v293(2) 1 October 2019

• Take home points of this consensus summary on the ethical use of AI in radiology:
• Ethical use of AI in radiology should promote wellbeing, minimize harm, and ensure that the benefits and harms are distributed among the possible stakeholders in a just manner.
• AI in radiology should be appropriately transparent and highly dependable, curtail bias in decision making, and ensure that responsibility and accountability remains with human designers or operators.
• The radiology community should start now to develop codes of ethics and practice for AI.
• Radiologists will remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem.

The global landscape of AI ethics guidelines

The global landscape of AI ethics guidelines
Nature Machine Intelligence v1, p389–399, 02 September 2019 [Not available on NHS OpenAthens]

• Analysis of the current corpus of principles and guidelines in ethical AI highlights five principles  (transparency, justice and fairness, non-maleficence, responsibility and privacy) but indicates variation in implementation.

Abstract

The potential for artificial intelligence in healthcare

The potential for artificial intelligence in healthcare
Future Healthc J. 2019 Jun; 6(2): 94–98. doi: 10.7861/futurehosp.6-2-94

• The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

How to stimulate effective public engagement on the ethics of artificial intelligence

How to stimulate effective public engagement on the ethics of artificial intelligence
Involve 29 March 2019

• Involve and DeepMind ran a series of three half-day roundtables in Autumn 2018 concentrating on public engagement, ethics, and artificial intelligence. The report ​outlines the findings and participant recommendations for working towards sustained public engagement on AI.