Search Tag: clinical AI
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 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 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 09 Jun
As the integration of artificial intelligence (AI) in healthcare advances, there is increasing emphasis on the need for robust methodological standards in model development and validation. Among these, the role of sample size in AI-based prediction modelling remains critically underappreciated. Unlike traditional clinical research such as randomised...Read more
2025 25 May
The emergence of digital medicine has been tightly interwoven with the proliferation of deep learning models, which require extensive and diverse datasets for effective development and validation. However, stringent privacy regulations, particularly in healthcare, limit the accessibility and sharing of real patient data. This tension between data...Read more
2025 25 May
Federated Learning (FL) offers a privacy-preserving solution for collaborative machine learning across decentralised electronic health record (EHR) datasets. However, the presence of covariate shifts—variations in patient demographics, clinical practices and data formats—poses a significant barrier to model generalisability. These shifts can compromise...Read more
2025 20 Apr
Artificial intelligence holds transformative potential for healthcare, particularly when applied through large language models (LLMs). These models promise enhanced administrative efficiency, better clinical decision-making and improved patient experiences. However, the value of these tools depends heavily on how well users can guide them—a process...Read more
2025 25 Mar
The increasing integration of artificial intelligence in healthcare and life sciences is fundamentally reshaping industries. One of the key aspects driving this transformation is the growing demand for high-quality, real-world data (RWD). As AI continues to evolve, its success is closely tied to the availability and management of authentic, comprehensive...Read more
2025 25 Mar
Hospitals are preparing to invest billions in artificial intelligence, but the path from pilot projects to system-wide implementation remains riddled with obstacles. As providers shift from experimental AI deployments to broader adoption strategies, the lack of clear evidence, standardised evaluation methods and foundational infrastructure creates...Read more
2025 10 Mar
The rise of artificial intelligence in healthcare has made data governance more critical than ever. AI-driven insights are only as reliable as the data they are built upon, making accuracy, security and integrity essential components of healthcare data management. Despite AI’s potential to transform clinical care and operational efficiency, many...Read more