Clinical Diagnostics

BrainADNet uses resting-state brain signals and graph convolutional networks to improve Major Depressive Disorder diagnosis and reveal sex-specific neural patterns.

How AI is changing diagnostics: benefits in radiology and pathology, risks like bias and automation bias, and practical steps for safe, clinician-centered adoption.

New AI-driven CSF liquid biopsy reads DNA methylation with M-PACT to identify brain tumors noninvasively. High accuracy, strong potential for pediatric care.

DeepRare, developed at Shenhua Hospital, raises rare disease diagnostic accuracy to 57% with clinical data and over 70% with genetics, offering traceable AI reasoning for clinicians worldwide.

DeepRare, developed by Shanghai Jiao Tong University and Xinhua Hospital, uses an agentic AI workflow to shorten rare disease diagnosis — outperforming Exomiser in Recall@1.

A concise assessment of AI for pulmonary embolism detection: reported accuracy in studies, why performance drops across hospitals, and steps for safer deployment.

Discover how the Health + AI Tech Show in London focuses on real-world AI adoption across clinical AI, genomics, drug discovery and hospital operations. Free registration for NHS staff and clinicians.

A concise overview of how AI is improving diagnostic speed and precision in imaging, pathology and clinical workflows, and what to expect next.

AI diagnostic tools in Africa are speeding accurate care, widening access where specialists are scarce, and prompting policies for local data, oversight, and sustainable funding.

A concise update on how AI is reshaping dermatopathology, its strengths for melanoma and common lesions, limits with rare diseases, and the path to safe clinical adoption.