Sunday, February 15, 2026
ISSN 2765-8767
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In this episode, the host discusses the significance of large language models (LLMs) in healthcare, their applications, and the challenges they face. The conversation highlights the importance of simplicity in model design and the necessity of integrating patient feedback to enhance the effectiveness of LLMs in clinical settings. Takeaways LLMs are becoming integral in healthcare. They can help determine costs and service options. Hallucination in LLMs can lead to misinformation. LLMs can produce inconsistent answers based on input. Simplicity in LLMs is often more effective than complexity. Patient behavior should guide LLM development. Integrating patient feedback is crucial for accuracy. Pre-training models with patient input enhances relevance. Healthcare providers must understand LLM limitations. The best LLMs will focus on patient-centered care. Chapters 00:00 Introduction to LLMs in Healthcare 05:16 The Importance of Simplicity in LLMs
The Future of LLMs in HealthcareDaily Remedy
YouTube Video U1u-IYdpeEk
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Clinical Reads

BIIB080 in Mild Alzheimer’s Disease: What a Phase 1b Exploratory Clinical Analysis Can—and Cannot—Tell Us

BIIB080 in Mild Alzheimer’s Disease: What a Phase 1b Exploratory Clinical Analysis Can—and Cannot—Tell Us

by Phil Galewitz
February 15, 2026
0

Can lowering tau biology translate into a clinically meaningful slowing of decline in people with early symptomatic Alzheimer’s disease? That is the practical question behind BIIB080, an intrathecal antisense therapy designed to reduce production of tau protein by targeting the tau gene transcript. In a phase 1b program originally designed for safety and dosing, investigators later examined cognitive, functional, and global outcomes as exploratory endpoints. The clinical question matters because current disease-modifying options primarily target amyloid, while tau pathology tracks...

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