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A defining year in the Aidence journey: Our 2022 highlights
This year, it became apparent that our bold ambitions can come true. Backed by a ... Continue reading...
Ten tips for running real-world research into the clinical utility of radiology AI
A longtime user of our AI solution, Veye Lung Nodules, once said he “feels naked ... Continue reading...
Who is the stricter regulatory parent?
Medical technology is rapidly evolving, challenging regulators to maintain oversight while also encouraging innovation. The ... Continue reading...
Catching up with lung cancer: AI and the post-pandemic imaging backlog
Covid-19 has brought profound changes to lung cancer care. In combination with staff shortages, it ... Continue reading...
From research to clinical practice: Building a body of evidence for radiology AI
When it comes to medical imaging AI, the proof is in the clinical studies. Or ... Continue reading...
The puzzling case of autonomous AI in medical imaging
“Welcome to the era of autonomous AI in medical imaging,” read an announcement from an ... Continue reading...
Anything but boring: The daily life of the QA/RA team
“I never thought I’d work in regulatory affairs.” Everyone in Quality Assurance or Regulatory Affairs ... Continue reading...
Lung cancer screening and the role of AI: Where are we?
Lung cancer screening is proven to save lives through early detection. But, countries in Europe ... Continue reading...
Do radiologists make better decisions with AI? The INPACT study is set to find out
Project INPACT is go!, reads a recent tweet from clinical consultancy Hardian Health. We have ... Continue reading...
From AI model to software medical device: Why the algorithm is only a fraction of the work
A good rule of thumb we can confirm from our own experience developing AI medical ... Continue reading...
The value of AI in radiology: From concept to physicians’ perception
A growing number of healthcare facilities are (re)organising based on the Value-Based Healthcare (VBHC) principles. ... Continue reading...
Why cloud computing is the best option for hospitals adopting AI
“Cloud computing” is something of a misnomer – a term wrongly applied due to a ... Continue reading...
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?
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 learnt 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
Medical device manufacturers often consider post-market surveillance (PMS) as an activity conducted by their regulatory ... 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...
Why patients and 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
In our previous regulatory article about 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
The adoption of medical imaging AI is about getting your hospital or screening programme ready ... 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
The covid-19 pandemic demands urgent action to manage the influx of patients. For AI companies, ... 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
The yearly World Lung Cancer Day and Lung Cancer Awareness Month are opportunities to raise awareness about ... 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 medical imaging: 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 By Kay, Machine Learning Engineer at Aidence. ... 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...