Tag: clinical AI
AI doesn’t know ‘no’ – and that’s a huge problem for medical bots
Toddlers may swiftly master the meaning of the word “no”, but many artificial intelligence models struggle to do so. They show a high fail rate when it comes to understanding commands that contain negation words such as “no” and “not”.That could mean medical AI models failing to realise that there is a big difference between an X-ray image labelled as showing “signs of pneumonia” and one labelled as showing “no signs of pneumonia” – with potentially catastrophic consequences if physicians rely on AI assistance to classify images when making diagnoses or prioritising treatment for certain patients.
It might seem surprising that today’s sophisticated AI models would struggle with something so fundamental. But, says Kumail Alhamoud at the Massachusetts Institute of Technology, “they’re all bad [at it] in some sense”. Learn more
Study shows vision-language models can’t handle queries with negation words
Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients.But if the model mistakenly identifies reports with both conditions, the most likely diagnosis could be quite different: If a patient has tissue swelling and an enlarged heart, the condition is very likely to be cardiac related, but with no enlarged heart there could be several underlying causes.
In a new study, MIT researchers have found that vision-language models are extremely likely to make such a mistake in real-world situations because they don’t understand negation — words like “no” and “doesn’t” that specify what is false or absent. Learn more
New method assesses and improves the reliability of radiologists’ diagnostic reports
Due to the inherent ambiguity in medical images like X-rays, radiologists often use words like “may” or “likely” when describing the presence of a certain pathology, such as pneumonia. But do the words radiologists use to express their confidence level accurately reflect how often a particular pathology occurs in patients? A new study shows that when radiologists express confidence about a certain pathology using a phrase like “very likely,” they tend to be overconfident, and vice-versa when they express less confidence using a word like “possibly.” Using clinical data, a multidisciplinary team of MIT researchers in collaboration with researchers and clinicians at hospitals affiliated with Harvard Medical School created a framework to quantify how reliable radiologists are when they express certainty using natural language terms. Learn more
Can deep learning transform heart failure prevention?
The ancient Greek philosopher and polymath Aristotle once concluded that the human heart is tri-chambered and that it was the single most important organ in the entire body, governing motion, sensation, and thought.Today, we know that the human heart actually has four chambers and that the brain largely controls motion, sensation, and thought. But Aristotle was correct in observing that the heart is a vital organ, pumping blood to the rest of the body to reach other vital organs. When a life-threatening condition like heart failure strikes, the heart gradually loses the ability to supply other organs with enough blood and nutrients that enables them to function.
Researchers from MIT and Harvard Medical School recently published an open-access paper in Nature Communications Medicine, introducing a noninvasive deep learning approach that analyzes electrocardiogram (ECG) signals to accurately predict a patient’s risk of developing heart failure. In a clinical trial, the model showed results with accuracy comparable to gold-standard but more-invasive procedures, giving hope to those at risk of heart failure. The condition has recently seen a sharp increase in mortality, particularly among young adults, likely due to the growing prevalence of obesity and diabetes. Learn more
