Clinical Diagnostics

AI is reshaping clinical care but also shifting responsibility for patient outcomes. Read a concise guide for clinicians and health leaders on human oversight, documentation, informed consent, and organisational risk steps to manage AI-related liability.

Roche’s Accu-Chek SmartGuide uses AI to predict and alert users to hypoglycemia, secures NHS reimbursement and supports Hypo Awareness Week 2026.

A 400-word snapshot of AI in medical diagnostics: current applications in imaging and pathology, patient and clinician benefits, integration hurdles, and near-term trends.

How AI and deep learning are detecting disease earlier than traditional methods, improving outcomes and lowering costs, with a focus on lung nodule detection in CT scans.

How AI analysis of routine ECGs can reveal hidden structural heart disease, improve detection in emergency departments, and what the SAGE trial means for clinical adoption.

Explore how pan-cancer AI, accelerated annotation, LLMs, and multi-modal models are transforming cancer diagnostics, prognosis, and treatment planning.

UK commits £20m to roll out AI for lung cancer detection and £8.1m for wider AI pilots, cutting diagnosis times and targeting national scale by 2029.

Cardiovolt.ai, an Imperial College London spinout, uses AI to extract novel digital biomarkers from routine ECGs, detecting heart failure, valve disease, diabetes, kidney disease and mortality risk with clinically relevant accuracy and a path to hospital deployment.

AI-driven expert systems, illustrated by VIAVI Solutions’ work, can automate diagnostic workflows, speed analysis and raise precision across clinical settings.

Discover how AI-driven functional imaging, including X-ray Velocimetry (XV), detects lung disease earlier than spirometry or CT and enables precision respiratory care for conditions like cystic fibrosis and asthma.