Skip To Main Content
John Powell on Muck Rack

John Powell

Arkansas
As seen in: Nature, Wiley Online Library, Patient Education and Counseling, PLOS ONE, Archives of Disease in Childhood, BMJ Open, MDPI, SAGE Publishing, Taylor & Francis Online, The BMJ and
Retired award-winning journalist and writer, PR and marketing professional, vaccine promoter for The Immunization Partnership
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with John

Contact John, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis

|
By James Leigh Verified, Angus W. Turner, Michael Kidd, John Powell, Catherine Pope, Alex Sherrington, Angus R. J. Barber
| Nature Verified
Abstract Artificial intelligence (AI) is an accurate screening tool for diabetic retinopathy (DR), the leading cause of blindness among working-aged adults. However, its impact on referral uptake is uncertain. We searched Embase, MEDLINE, Scopus, Web of Science and Cochrane Library databases from year 2000 to February 17, 2025. Randomised and non-randomised studies comparing referral uptake after AI-assisted DR screening versus standard of care were included.
Open in Who Shared Issue with article?

When to and when not to use machine learning in risk prediction models

|
By Lei Clifton, John Powell, David A Clifton
| The Lancet Verified
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine learning or classical medical statistics for medical research is long standing.1,2 Given major methodological advances and increased policy attention, renewed consideration is warranted for when machine learning should and should not be used in risk prediction models.
Open in Who Shared Issue with article?

Unintended consequences of using ambient scribes in general practice

|
By Abi Eccles, Catherine Pope, John Powell, Tom Pelly
| The BMJ Verified
Abi Eccles, senior researcher of digital health1, Tom Pelly, general practitioner2, Catherine Pope, professor of medical sociology1, John Powell, professor of digital healthcare1 1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK 2Horfield Health Centre, Bristol, UK Correspondence to: A Eccles abi.eccles{at}phc.ox.ac.uk Abi Eccles and colleagues argue that better evidence is crucial to ensure widespread adoption of ambient scribes does not compromise...
Open in Who Shared Issue with article?
See all 93 licensed articles (95 Total)