Using smartly trained neural networks, researchers at TU Graz have succeeded in generating precise real-time images of the beating heart from just a few MRI measurement data.
In a recent study, researchers proposed a novel technique to help make stained histopathological image datasets more useful for many emerging machine-learning-based classification systems.
With enough medical training data, AI can predict health conditions with astounding accuracy. Now, researchers want to use brain scans of the entire Scottish population to have an algorithm early detect dementia.
Can an AI predict the progression of multiple sclerosis in a patient over the subsequent months and years? Researchers in Belgium used machine learning to do just that – with promising results.
A combination of facial thermal imaging and artificial intelligence (AI) can accurately predict the presence of coronary artery disease (CAD), new research finds.
Chatbots like ChatGPT generally deliver servicable results when asked for healthcare advice. However, new research suggests that the LLM's accuracy drops when languages other than English are used.
As AI is playing an increasingly crucial role in the fields of pharmacy and medicine, Pharmaceutical Automation and Digitalisation Congress (AUTOMA+ 2024) welcomes the entire industry value chain to join the roundtable discussion on the innovative strategies that are navigating the future of pharma. The Congress takes place in Zurich, Switzerland, on 18-19 November 2024.
Matching the right donor heart to the right recipient at the right time is a complex task. Now, experts point out how AI can provide unbiased decision-support for transplantation process.
Some year in this decade, AI tools will become ubiquitous within clinical laboratories. AI has the potential to increase the accuracy of laboratory testing and improve the quality and efficiency of operations and service of testing labs.
Warfarin is sometimes prescribed after heart surgery, but getting the dose right requires a personalised approach for each patient. A new AI tool is designed to help with this complex task.
Researchers work on the first prototype that applies AI to colorectal diagnosis. The prototype achieved a diagnostic acuity of 93.44% and a sensitivity of 99.7% in the detection of high-risk lesions.
Not all medication can safely be taken together. Using a machine-learning algorithm, researchers predict interactions that could interfere with a drug’s effectiveness.
AI models are highly capable in analysing tissue samples – as long as conditions are lab-perfect. Add a little contamination, however, and diagnostic accuracy goes out the window, a new study shows.
Researchers have developed software able to disguise sensitive data in health care applications. This protects privacy while making datasets available for development of better treatments.
A feasibility study by Swiss researchers shows that AI systems are able to reliably predict therapeutic success in patients with borderline personality pathology by detecting emotions on their face.
Can AI help better evaluate images of brain tumours? A publication from German researchers on this topic presented at this years' ICIS conference won the Best Paper Award.
A powerful tool, but the need for human judgment remains: In an editorial published in PNAS Nexus, Monica M. Bertagnolli assesses the promise of AI and machine learning to study and improve health.
Sure, AI still has a long way go. But maybe one day in the not-so-distant future, AI will provide us with information about our current state of health, such as the number of red blood cells, cholesterol levels, fat percentage, and how many seconds last night's beer will shorten our life expectancy.
Machine learning and AI are playing an increasingly important role in medicine and healthcare, and not just since ChatGPT. This is especially true in data-intensive specialties such as radiology, pathology or intensive care. The quality of diagnostics and decision-making via AI, however, does not only depend on a sophisticated algorithm but – crucially – on the quality of the training data.
