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How to move fast and not break things
On May 26th 2021, the EU Medical Device Regulation 2017/745 (MDR) passed the date of ... Continue reading...
Quality assurance in lung cancer screening programmes
There is a bigger health crisis at the end of covid-19: the cancer backlog. In ... Continue reading...
The partnerships, upgrades, articles and events that defined 2021
It was the best of times; it was the worst of times, one could say, ... Continue reading...
Bias in medical imaging AI: Checkpoints and mitigation
This year, the medical imaging AI industry was shaken by research showing that an AI ... Continue reading...
AI and radiologists: friends, rivals, unresolved? Five physicians weigh benefits and limitations
Artificial intelligence (AI) in medical imaging is a topic of much debate and insufficient consensus. ... Continue reading...
The delicate balance between data protection and availability
I recently got to say the four magic words: graduated and a job! My master’s ... Continue reading...
Lessons learned supporting the NHS England Targeted Lung Health Checks
Lung cancer is a devastating illness. It kills one person every 30 seconds and costs ... Continue reading...
Quality improvement in theory and practice: Guiding principles and a real-world incident fix
As a clinician, I was always interested in quality improvement. I enjoyed the logic of ... Continue reading...
In a race between AI algorithms, the best software will win
The inspiration for this article came from a tweet by Dr Amine Korchi, neuroradiologist and ... Continue reading...
A new era of post-market surveillance for AI medical solutions: Live performance monitoring
PMS as a collaborative effort Medical device manufacturers often consider post-market surveillance (PMS) as an ... Continue reading...
What lung cancer screening can learn from breast screening programmes
A speaker I find most inspiring is Liz O’Riordan. She is a breast surgeon who ... Continue reading...
Making a case for buying medical imaging AI: How to define the return on investment
There have been many claims about technologies making healthcare better or more affordable. Artificial intelligence ... Continue reading...
An accelerated workflow change: What a radiologist thinks about working from home
Remote work in radiology has traditionally been for the lucky few. The reasons to facilitate ... Continue reading...
Patient engagement: Why the public should be part of the conversation around AI in healthcare
As a physician, I learned that the patients are the experts. They live with their ... Continue reading...
The (not-so-distant) future of AI in healthcare
There have been many claims about technologies making healthcare better or more affordable. Artificial intelligence ... Continue reading...
Cybersecurity for software medical devices: An overview of regulatory frameworks
In our previous regulatory article around the introduction of the MDR and its impact on ... Continue reading...
Where does medical imaging AI have the most impact today?
There have been many claims about technologies making healthcare better or more affordable. Artificial intelligence ... Continue reading...
The five-step guide to AI adoption in clinical practice
Adoption of medical imaging AI is about getting your hospital or screening programme ready to ... Continue reading...
How do users’ perceptions of AI influence adoption into value-based radiology departments?
Artificial intelligence (AI) developments in healthcare present time-saving benefits for physicians, by increasing productivity and ... Continue reading...
Will the MDR improve regulatory oversight of AI solutions?
The regulatory frameworks are often unclear when it comes to software as a medical device, ... Continue reading...
Lung cancer care in the covid-19 aftermath
Healthcare practitioners continue to deserve our applause for their immense dedication to taking care of ... Continue reading...
Is AI the key to effective and sustainable lung cancer screening? (Part 2)
Last year, I felt like I’d let people down after deciding to leave my clinical radiology training ... Continue reading...
The roadmap to a safe and sustainable AI medical solution, during and beyond covid-19
All hands on deck The covid19 pandemic demands urgent action to manage the influx of ... Continue reading...
Workflow integration is the game-changer for medical imaging AI
The potential extra burden of AI Medical imaging technology is increasingly sophisticated, for a large ... Continue reading...
Is AI the key to effective and sustainable lung cancer screening?
Last year, I felt like I’d let people down after deciding to leave my clinical radiology training ... Continue reading...
Veye Lung Nodules “frees up time” for radiologists to focus on more complex cases
The challenge at Tergooi The high pressure on radiologists is showing no signs of easing. ... Continue reading...
The Veye Lung Nodules journey: from sceptical to “one of the biggest fans”
The challenge at Northwest Clinics The benefits of CT lung screening have recently been reconfirmed ... Continue reading...
Veye Lung Nodules “comes at the right time” for radiologists at Liverpool hospital
The challenge at the Liverpool Heart and Chest Hospital The Liverpool Heart and Chest Hospital ... Continue reading...
Early lung cancer detection with AI: A guide for patients
World Lung Cancer Day and Lung Cancer Awareness Month are opportunities to raise awareness about the leading ... Continue reading...
Workload in radiology: Why should we care?
Headlines such as ‘Radiology on the brink of collapse‘ and ‘Urgent shortage of radiologists’ resonated ... Continue reading...
AI in healthcare: Why enough quality data trumps good models
When you’re a data scientist, conferences are a great time to reload on state-of-the-art knowledge ... Continue reading...
Why I left clinical practice, but not healthcare
Being a doctor gave me the ultimate sense of purpose. I’d always loved the idea ... Continue reading...
Read, watch, listen: Recommendations from the Aidence team
Meta-learning for medical imaging: where are we after ICML Theranos: a cautionary tale for medical ... Continue reading...
Group-Convolutions: Overcoming the data challenge in medical image analysis
A data challenge fit for AI Artificial intelligence has the opportunity to cause a huge ... Continue reading...
Regulations are rapidly evolving, but do they match technology?
At first sight and consideration, one may say that new solutions driven by artificial intelligence ... Continue reading...