Clinical literature reviews and commentary on AI in medicine, geriatrics, dementia biomarkers, miRNA research, and the realities of translating research into older-adult care.
Does extracting structured linguistic features with large language models (LLMs) actually lead to earlier or more accurate identification of Alzheimer’s disease, and is the approach interpretable enough for clinical trust and use?
With anti-amyloid therapies on the menu, does amyloid PET scanning deliver enough diagnostic and therapeutic precision to justify its complexity, cost, and infrastructural demands?
When curating real-world imaging cohorts or automating radiology workflows, do large language models, rule-based systems, or radiologists produce the most accurate labels—and what hybrid mixes make sense?
What happens—ethically and practically—when large language models start explaining clinical findings directly to patients and families, particularly in nursing-led communication?
Can sparse autoencoders offer the kind of actionable interpretability for large language models that would make clinicians more comfortable relying on these models?
Can the latest multimodal GPT models, used at the bedside in real time, accurately predict functional outcomes for intracerebral hemorrhage—and do their calibration and reproducibility pass muster for routine clinical decisions?