Search Tag: clinical AI
2025 10 Nov
Referring physicians who rely on radiology services are central to how artificial intelligence is adopted in clinical pathways. A survey of licensed doctors in Germany explored how these clinicians view AI in radiological diagnosis, what they trust and which applications they value most. The sample included internists, surgeons and general practitioners,...Read more
2025 10 Nov
Rising MRI volumes and increasingly complex examinations continue to pressure radiology services. Responding to this demand, researchers developed a deep learning (DL) model to analyse routine knee MRI and assessed its value for resident radiologists. The model targets 23 conditions across cartilage, menisci, bone marrow, ligaments and other soft...Read more
2025 18 Sep
Large language models are increasingly embedded in radiology tasks such as report generation, interpretation and workflow optimisation. Their value depends less on model scale alone and more on the clarity, context and structure of the inputs that guide them. Prompt engineering aligns model behaviour with clinical intent, curbs irrelevant outputs...Read more
2025 01 Aug
Radiology report generation aims to automate the conversion of medical images into clinically relevant textual descriptions. This task carries unique challenges compared to general image captioning due to the complexity and specificity of medical language, the need for clinical accuracy and the imbalance in data distribution. In clinical datasets,...Read more
2025 15 Jun
Radiology has long relied on expert annotations to enrich medical imaging data for research and training artificial intelligence systems. These annotations, created by specialists, have traditionally been applied manually—an effort-intensive process that limits scalability. As the demand for large-scale, labelled datasets grows, so does the need...Read more
2024 03 Jul
The current landscape of artificial intelligence (AI) in clinical diagnostics is characterised by significant advancements and the ongoing refinement of AI tools, particularly in imaging and pathology. As these proprietary systems evolve, the focus is shifting from mere diagnostic accuracy to differentiating between comparable AI tools to enhance...Read more