Certain genetic features are crucial for treatment decisions for AML leukaemia. A team from Münster shows how an AI-based method can predict these features from images of bone marrow smears.
Research from Shenzhen proposes an integrated diagnosis model for automatic classification of adult-type diffuse gliomas directly from annotation-free standard whole-slide pathological images.
A new deep-learning approach to AMR testing has been shown to detect antimicrobial susceptibility within as little as 30 minutes - significantly faster than current gold-standard approaches.
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.
Researchers from Finland have developed an artificial intelligence tool for automatic colorectal cancer tissue analysis that outperforms prior methods.
A new AI method for CT brain imaging may bring the modality to the level of detail usually reserved for MRI scans. This could enhance diagnostic support for conditions such as Alzheimer's disease.
A new artificial intelligence (AI) model combines imaging information with clinical patient data to improve diagnostic performance on chest X-rays, a new study finds.
For the first time, researchers show that AI-based predictions can deliver comparable results to clinical tests on biopsies of patients with colorectal cancer (CRC).
Osaka Metropolitan University scientists have developed an advanced AI model that utilizes chest radiographs to accurately estimate a patient’s "true" age.
Researchers from the University of Chicago developed a deep-learning model to assess chest X-ray radiographs for probable Covid-19 severity.
Experts have highlighted how precision pathology using Artificial Intelligence can provide an effective alternative to molecular diagnostics. This, say a team from the Karolinska Institutet (KI) in Stockholm, Sweden, can also offer multiple advantages within a clinical setting and support risk stratification.
A new AI model finds that x-ray images collected during routine medical care can provide warning signs for diabetes, even in patients who don’t meet the guidelines for elevated risk.
Researchers at Monash University have designed a new co-training AI algorithm for medical imaging that can effectively mimic the process of seeking a second opinion.
A new study suggests that deep learning–based computer-aided diagnosis (CAD) for breast lesion classification on ultrasound can significantly improve radiologists' diagnostic performance.
A team of researchers from Denmark have developed a new AI framework to address the number of strokes that go unrecognised by human emergency call handlers.
The latest version of ChatGPT passed a radiology board-style exam, highlighting the potential of large language models (LLM) but also revealing limitations that hinder reliability, new research shows.
Messenger Ribonucleic Acid (mRNA) as a therapeutic approach is gaining momentum due to its ability to be rapidly manufactured and its promising outcomes.
A new paper describes how generative AI text-to-image models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation in health care.
The distinction between primary tumors and metastases can be made quickly and accurately in brain tumors using radiomics and deep learning algorithms, a new study shows.
