As the global burden of Alzheimer's disease (AD) continues to grow, early and accurate detection has become increasingly critical, especially in regions with limited access to advanced diagnostic tools. We propose BRAINS (Biomedical Retrieval-Augmented Intelligence for Neurodegeneration Screening) to address this challenge. This novel system harnesses the powerful reasoning capabilities of Large Language Models (LLMs) for Alzheimer's detection and monitoring. BRAINS features a dual-module architecture: a cognitive diagnostic module and a case-retrieval module. The Diagnostic Module utilizes LLMs fine-tuned on cognitive and neuroimaging datasets -- including MMSE, CDR scores, and brain volume metrics -- to perform structured assessments of Alzheimer's risk. Meanwhile, the Case Retrieval Module encodes patient profiles into latent representations and retrieves similar cases from a curated knowledge base. These auxiliary cases are fused with the input profile via a Case Fusion Layer to enhance contextual understanding. The combined representation is then processed with clinical prompts for inference. Evaluations on real-world datasets demonstrate BRAINS effectiveness in classifying disease severity and identifying early signs of cognitive decline. This system not only shows strong potential as an assistive tool for scalable, explainable, and early-stage Alzheimer's disease detection, but also offers hope for future applications in the field.

Patients have long sought health information online, and increasingly, they are turning to generative AI to answer their health-related queries. Given the high stakes of the medical domain, techniques like retrieval-augmented generation and citation grounding have been widely promoted as methods to reduce hallucinations and improve the accuracy of AI-generated responses and have been widely adopted into search engines. This paper argues that even when these methods produce literally accurate content drawn from source documents sans hallucinations, they can still be highly misleading. Patients may derive significantly different interpretations from AI-generated outputs than they would from reading the original source material, let alone consulting a knowledgeable clinician. Through a large-scale query analysis on topics including disputed diagnoses and procedure safety, we support our argument with quantitative and qualitative evidence of the suboptimal answers resulting from current systems. In particular, we highlight how these models tend to decontextualize facts, omit critical relevant sources, and reinforce patient misconceptions or biases. We propose a series of recommendations -- such as the incorporation of communication pragmatics and enhanced comprehension of source documents -- that could help mitigate these issues and extend beyond the medical domain.

There is much discussion on the processing needed to propel further advancement in artificial intelligence (AI). Among others exploring this space, Henry Kautz proposed a taxonomy for neural-symbolic computing, parsing the integration of differentiable and symbolic information at the processing level and introducing six types of systems. In this series of blogs, I offer a different perspective: an information-centric classification with emphasis on the type, structure, and representation of knowledge and its implications for the attributes of the systems that deploy them. Such classification is needed for categorizing and assessing the structure and representation of the information integrated as AI systems learn and perform. In this blog, I will offer that an information-centric characterization of solutions can bring clarity to the underlying choices made and their material implications on the quality and efficiency of AI systems.

Gadi Singer is Vice President and Director of Emergent AI Research at Intel Labs leading the development of the third wave of AI capabilities. The previous blog in this series introduced the concept of an information-centric classification of AI systems as a highly valuable view that is complementary to processing-based classifications such as Henry Kautz' taxonomy for neural symbolic computing. It also previewed a classification that emphasizes the high-level architectural choice related to information in the AI system. The first class of systems in this classification system with its'Fully Encapsulated Information' was detailed in the previous blog of this series. Systems in this class incorporate all information required for AI tasks in the weights and model parameters without leveraging any additional adjunct sources of information.