Search Tag: clinical AI
2025 17 Nov
Magnetic resonance imaging guides prostate cancer diagnosis, yet availability, variability, workflow and cost can limit its use. Multiparametric ultrasound (mpUS) supported by artificial intelligence offers a different route that may be easier to implement at scale. A three-dimensional mpUS model was evaluated for its ability to localise clinically...Read more
2025 10 Nov
Artificial intelligence reshapes clinical practice, yet its paediatric use remains limited and unevenly governed. Evidence shows a pronounced gap between adult and paediatric applications, alongside growing regulatory attention to software as a medical device. Paediatric care adds challenges spanning assent, privacy, data scarcity and thRead more
2025 10 Nov
Artificial intelligence reshapes clinical practice, yet its paediatric use remains limited and unevenly governed. Evidence shows a pronounced gap between adult and paediatric applications, alongside growing regulatory attention to software as a medical device. Paediatric care adds challenges spanning assent, privacy, data scarcity and thRead more
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
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
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 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 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 09 Nov
Large language models (LLMs) are moving into clinical decision support, yet their value for personalised recommendations remains uncertain. A new benchmark focused on longevity interventions examines how different models perform when asked to generate advice based on biomarker profiles. Using synthetic cases that mirror common scenarios in geroscience,...Read more
2025 09 Nov
Large language models (LLMs) are moving into clinical decision support, yet their value for personalised recommendations remains uncertain. A new benchmark focused on longevity interventions examines how different models perform when asked to generate advice based on biomarker profiles. Using synthetic cases that mirror common scenarios in geroscience,...Read more
2025 09 Nov
Large language models (LLMs) are moving into clinical decision support, yet their value for personalised recommendations remains uncertain. A new benchmark focused on longevity interventions examines how different models perform when asked to generate advice based on biomarker profiles. Using synthetic cases that mirror common scenarios in geroscience,...Read more
2025 07 Nov
Artificial intelligence is accelerating progress across biotechnology and digital medicine by linking genomic, clinical and imaging data to generate more comprehensive insights than any single source alone. Multimodal approaches are now informing target identification, molecule design, clinical trial optimisation and imaging-based diagnosis, with...Read more
2025 07 Nov
Artificial intelligence is accelerating progress across biotechnology and digital medicine by linking genomic, clinical and imaging data to generate more comprehensive insights than any single source alone. Multimodal approaches are now informing target identification, molecule design, clinical trial optimisation and imaging-based diagnosis, with...Read more
2025 07 Nov
Artificial intelligence is accelerating progress across biotechnology and digital medicine by linking genomic, clinical and imaging data to generate more comprehensive insights than any single source alone. Multimodal approaches are now informing target identification, molecule design, clinical trial optimisation and imaging-based diagnosis, with...Read more
2025 07 Nov
Artificial intelligence is accelerating progress across biotechnology and digital medicine by linking genomic, clinical and imaging data to generate more comprehensive insights than any single source alone. Multimodal approaches are now informing target identification, molecule design, clinical trial optimisation and imaging-based diagnosis, with...Read more
2025 07 Nov
Artificial intelligence is accelerating progress across biotechnology and digital medicine by linking genomic, clinical and imaging data to generate more comprehensive insights than any single source alone. Multimodal approaches are now informing target identification, molecule design, clinical trial optimisation and imaging-based diagnosis, with...Read more
2025 16 Oct
Artificial intelligence is advancing quickly in medical imaging, expanding potential users and use cases while exposing gaps in knowledge about capabilities, risks and deployment. Complex models, large data demands and distinct non-human failure modes make safe adoption challenging. A multisociety syllabus from several institutions sets out role-specific...Read more
2025 24 Sep
Biomedical foundation models are increasingly being integrated into healthcare, but their evaluation methods often fail to reflect real-world conditions. Performance can deteriorate when data sources, clinical workflows or user interactions shift, creating risks for patient safety and clinical decision-making. An analysis of more than 50 biomedical...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 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 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 13 Sep
Healthcare systems spend heavily on chronic disease once symptoms emerge, yet many conditions are preventable or better managed with earlier intervention. Artificial intelligence is shifting the balance by supporting earlier diagnoses, generating actionable insights and improving how resources are allocated. At the HIMSS25 Global Conference, healthcare...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 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 06 Jul
Emergency departments (EDs) face increasing challenges related to patient flow, overcrowding and resource allocation. Predicting key outcomes such as patient length of stay (LOS) and disposition decision (DD) can significantly improve operational efficiency. However, existing machine learning (ML) models often lack transparency and transferability,...Read more
2025 06 Jul
Emergency departments (EDs) face increasing challenges related to patient flow, overcrowding and resource allocation. Predicting key outcomes such as patient length of stay (LOS) and disposition decision (DD) can significantly improve operational efficiency. However, existing machine learning (ML) models often lack transparency and transferability,...Read more
2025 06 Jul
Emergency departments (EDs) face increasing challenges related to patient flow, overcrowding and resource allocation. Predicting key outcomes such as patient length of stay (LOS) and disposition decision (DD) can significantly improve operational efficiency. However, existing machine learning (ML) models often lack transparency and transferability,...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
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