Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
United States healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias (ADRD). Generative AI built on language models (LLMs) now enables agentic AI systems that can enhance clinician capabilities to approach specialist-level assessment and decision-making in ADRD care at scale. This article presents a comprehensive six-phase roadmap for responsible design and integration of such systems into ADRD care: (1) high-quality standardized data collection across modalities; (2) decision support; (3) clinical integration enhancing workflows; (4) rigorous validation and monitoring protocols; (5) continuous learning through clinical feedback; and (6) robust ethics and risk management frameworks. This human centered approach optimizes clinicians' capabilities in comprehensive data collection, interpretation of complex clinical information, and timely application of relevant medical knowledge while prioritizing patient safety, healthcare equity, and transparency. Though focused on ADRD, these principles offer broad applicability across medical specialties facing similar systemic challenges.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 122 references that are in the map. First posted on arXiv 6 Feb 2025.
Builds on
12 of 122 references.
References · 12
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
- 2022Large Language Models Encode Clinical Knowledgecited by 1,830
- 2023Towards Generalist Biomedical AIcited by 289
- 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
- 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
- 2024Capabilities of Gemini Models in Medicinecited by 237
- 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 234
- 2023Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicinecited by 83
- 2024Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classificationcited by 70
- 2024De-identification is not enough: a comparison between de-identified and synthetic clinical notescited by 14
- 2021Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?cited by 10
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026AI-Care: A Conversational Agentic System for Task Coordination in Alzheimer's Disease Care
- 2026AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study
- 2025AgenticAD: A Specialized Multiagent System Framework for Holistic Alzheimer Disease Management
- 2025Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care
- 2026Dementia-Agents: A Multi-Modal Multi-Agent System for Dementia Staging and Phenotyping
- 2025Designing and Evaluating a Conversational Agent for Early Diagnosis of Alzheimer's Disease and Related Dementias
- 2026Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment
- 2025ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator