Your examples help show how evidence and education become better outcomes for patients, professionals, and communities. They also help others learn what works.

What changed in your setting or community

How BMJ helped (journal, event, course, guideline, BMJ Best Practice clincial decision support, or another tool)

Who benefited (patients, clinicians, students, policy makers)

Any evidence you can share (numbers or qualitative feedback)

Example: “Using BMJ Best Practice, our team reduced time to correct diagnosis for neonatal sepsis.”

 “The more we show what’s working, the faster good ideas spread.”

 Ingrid Bray
 Senior Corporate Communications Manager, and editor of BMJ Group’s annual impact report 

Participants who completed the training modules demonstrated significantly improved ECG interpretation skills, with improved sensitivity and specificity for detecting potentially dangerous cardiac abnormalities in athletes.

Professor Jonathan Drezner 
Director of the UW Medicine Center for Sports Cardiology at the University of Washington and editor in chief of the British Journal of Sports Medicine

At BMJ Group, our Digital Health business area produces a range of digital resources for clinical decision support and medical education, such as BMJ Best Practice. We are witnessing a rapidly increasing interest in how our digital resources can be used in the context of machine learning and artificial intelligence. In response, we are ensuring that our resources contribute to these innovations.

In doing so, however, we are equally keen to ensure that our actions are ethical and safe. This is especially so in the area of active clinical decision support. That’s why we have a dedicated team of external experts we can rely on to help guide our decisions.

Members of the expert committee on ethics and artificial intelligence advise us on issues such as avoiding harm, patient autonomy, transparency and explicability, human agency and oversight, safety, security, legality, confidentiality, or data governance. The committee is advisory—Digital Health is responsible for its decisions.

The committee has several roles

1. Clarifying, reviewing, and developing policies relating to artificial intelligence, clinical decision support, and digital health

2. Advising the BMJ Digital Health team on ethical questions that might arise during existing or new lines of work

3. Advising the BMJ Digital Health team on new partnerships that it might develop with organisations that are creating artificial intelligence software

4. Taking part in roundtables on important issues or controversies related to artificial intelligence in healthcare

How the committee works

Members of our ethics committee correspond and meet on an as-needed basis when our BMJ Digital Health business area needs advice. Individuals are contacted based on their expertise in areas such as avoiding harm, patient autonomy, transparency and explicability, human agency and oversight, safety, security, legality, confidentiality, or data governance. The group will also meet—but no more than once per year. Meetings occur at the London head office office in the UK, with remote participation possible.

Ethics committee members

• Louise Crowe, Director Customer & Markets, Digital Health, BMJ Group (Chair)
• Yvonne Ridge, Head of Product, Digital Health, BMJ Group
• Chris Wroe, Head of Informatics, Digital Health, BMJ Group
• Kieran Walsh, Clinical Director, BMJ Group
• Helen MacDonald, Research Integrity Editor, The BMJ
• Timothy Morgan, Data Protection Compliance Manager, BMJ Group (involved as expertise required)
• Clare Robinson, Lawyer, BMJ Group (to be involved as expertise required)

• Dipak Kalra, President, The European Institute for Innovation through Health Data; Professor of Health Informatics, UCL; Visiting Professor, University of Ghent
• Dr Anirudh Kumar, Specialty Doctor in Acute Medicine, Barnsley Hospital NHS Foundation Trust
• Jools E Symons, Patient and Public Involvement Manager, Communication Skills Lead, Lived Experience Network Lead, University of Leeds
• Nazrul Islam, Research Editor, The BMJ
• Sara Payne, Health Regulation and Ethics
• Ellis Parry, Director, Data Privacy Compliance Solutions Ltd
• Malcolm Duncan, Healthcare Information Technology Consultant, BMJ Group
• Observers: Up to two members from BMJ Digital Health may join the meetings as observers

• Trusted AI 101: A Guide to Building Trustworthy and Ethical AI Systems
• AI4People—An Ethical Framework for a Good AI Society
• AI4People – 7 AI Global Frameworks 
• Understanding artificial intelligence ethics and safety
• WHO guidance

• OECD Principles on AI
• State of implementation of the OECD AI principles
• UK Centre for Data Ethics and Innovation (CDEI)
• Safe and trusted AI 
• Big data, artificial intelligence, machine learning and data protection
• Ethics and governance of artificial intelligence for health

• Ethics Framework
• Good Machine Learning Practice for Medical Device Development: Guiding Principles 
• NHSX: Artificial Intelligence: How to get it right. Putting policy into practice for safe data-driven innovation in health and care 
• Recommendation on the ethics of artificial intelligence

“As we embrace the potential of artificial intelligence in healthcare, we remain steadfast in our focus on ensuring that the innovations we create are ethical, safe, and patient focused. Our work in the Digital Health business area at BMJ Group combines cutting-edge technology with rigorous ethical oversight to support clinicians and improve health outcomes worldwide.”

Yvonne Ridge
Head of Product, Digital Health, BMJ Group

At BMJ Group, our approach to using artificial intelligence (AI) responsibly, ethically, sustainably, and safely is guided by four principles: transparency and accountability, integrity and quality control, privacy and data security, and collaboration and inclusivity. 

As AI technology rapidly evolves, we will adapt our approach, procedures, and governance to meet new challenges with the most sustainable options and industry best practices.

Transparency is central to our workflows, including the use of AI. All AI-generated content must be disclosed, with identifiable steps and retrievable data for validation and accountability. We hold BMJ Group staff accountable for verifying and overseeing AI outputs, always prioritising human review and responsibility in all workflows involving AI.

Our quality control standards ensure AI enhances, not replaces, the processes behind delivering trusted healthcare information. Dedicated experts verify all AI-driven contributions to meet evidence-based standards, prioritising patient safety and clinical accuracy in all AI applications.

We strictly adhere to data security policies to protect internal and customer information. Our use of AI is always responsible, adhering to data protection law.  

Promoting a collaborative approach to AI development and usage is embedded within BMJ Group’s culture. As strong advocates of a world that supports equity, diversity and inclusiveness, diverse perspectives are always considered to meet all communities’ and stakeholders’ needs equitably.