Skip To Main Content
Ewen Harrison on Muck Rack

Ewen Harrison

As seen in: MSN UK, Nature, BMJ Open, The BMJ, The Conversation, Medical Xpress, Routledge, The Lancet, Inkl, Tolerance Canada and
The Surgical Outcomes Club was launched at ASGBI's 2013 International Surgical Congress in Glasgow.
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 Ewen

Contact Ewen, 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

AI in the emergency department: promising, powerful but still unproven

|
By Ewen Harrison
| MSN UK Verified
Ewen Harrison, Professor of Surgery and Data Science, University of Edinburgh The two doctors used for comparison managed 50% and 55%. That's a meaningful gap, especially at the moment when information is scarcest and uncertainty is highest. This new study goes further. It puts AI alongside doctors across several tasks, using genuine clinical text from a real emergency department. That makes it more directly relevant to medical practice than most of what's come before.
Open in Who Shared View Duplicates (8) Issue with article?

AI in the emergency department: promising, powerful but still unproven

|
By Ewen Harrison
| Tolerance Canada
Artificial intelligence can now outperform doctors at diagnosing patients in the emergency department, according to a new study in Science. The AI was given written notes from real emergency department records from a hospital in Boston, US, and asked to weigh in at different points during the patient’s care. At the earliest stage – triage, when a patient first arrives – the AI identified the correct diagnosis, or something… Read complete article © The Conversation -
Open in Who Shared Issue with article?

ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks

|
By Yinghao Zhu, Junyi Gao, Zixiang Wang, Weibin Liao, Xiaochen Zheng, Lifang Liang, Miguel O. Bernabeu, Yasha Wang, Lequan Yu, Chengwei Pan, Ewen Harrison, Liantao Ma
| Nature Verified
Abstract Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to be inferior to specialized models, creating potential for misuse and misunderstanding.
Open in Who Shared Issue with article?
See all 46 licensed articles (48 Total)