Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease

Affiliations: Goizueta Brain Health Institute, Emory University; Department of Radiology and Biomedical Imaging, UCSF; School of Medicine, UCSF; Bakar Computational Health Sciences Institute, UCSF; Memory and Aging Center, Department of Neurology, UCSF; Department of Neurology, Weill Institute for Neuroscience, UCSF; NSF AI Institute for Advances in Optimization (AI4OPT), Georgia Institute of Technology; Department of Neuroscience, UC Berkeley; Division of Biostatistics, UC Berkeley; Center for Intelligent Imaging (ci2), UCSF; Department of Psychiatry and Behavioral Sciences, UCSF

"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."

"The gap between the demand for neurological care and the number of neurology providers continues to grow, leading to long wait times, delayed diagnoses, and increased emergency department visits. Neurological diagnoses are difficult to make for a wide variety of reasons ranging from the great variability and complexity in patient presentations to the extensive time and knowledge required to effectively collect and interpret necessary data. These challenges are amplified by limited office visit time, exponentially growing data complexity, and an overwhelming volume of medical literature and research opportunities that exceed any individual clinician's capacity to master."

"Simultaneously, artificial intelligence (AI) is rapidly advancing and has entered a new era through generative AI built on large language models (LLMs). A growing number of studies have demonstrated that LLM-based systems encode clinical knowledge, can achieve expert-level medical question answering, integrate multimodal patient data, are capable of empathetic history taking through conversational agents, and enhance clinical decision-making. LLMs have enabled agentic AI systems, architectures in which one or more LLMs coordinate interacting components such as traditional discriminative AI models and tools to perform complex, multistep tasks."

"While much research has focused on AI's potential to identify patterns beyond human perception (predictive AI), clinical implementation remains challenging because clinicians cannot readily verify insights they cannot detect, nor can these systems naturally complement clinical workflows. However, agentic AI systems offer more immediately practical applications by enhancing clinicians' capabilities in three critical areas where healthcare providers increasingly struggle: comprehensive data collection, interpretation of increasingly complex multimodal information, and timely, context appropriate application of relevant medical knowledge."

"Currently, we can deploy domain-specific AI assistants for individual functions, each optimized for its particular role in data collection, interpretation, or knowledge retrieval; however, the future vision involves an integrated agentic AI system capable of seamlessly coordinating these functions within a continuously learning healthcare framework. Such systems would leverage every patient encounter while incorporating the latest clinical evidence."

"Still, such a learning system must be intentionally molded to benefit diverse patient populations, support both patients and providers without imposing new burdens, and be safely implemented in any clinic setting. Recent concerns around generative and agentic AI have emphasized the need for strict boundaries around autonomy and for maintaining robust human oversight in high-stakes medical contexts. This is particularly important in ADRD, where vulnerable populations, shifting decision-making capacity, and high-stakes diagnostic uncertainty could magnify risk. Therefore, we advocate for constrained agentic AI systems in which clinicians remain in the loop, supported by rigorous evaluation and continuous monitoring using clinically meaningful outcomes."

"In this perspective article, we provide a roadmap for how responsible agentic AI integration can be accomplished in the field of ADRD in the United States and scale specialist-level clinical care. Our roadmap consists of six interconnected phases: (1) high-quality standardized data collection, (2) AI decision support development, (3) clinical integration and workflow, (4) validation and monitoring, (5) continuous learning, and (6) ethics and risk management. While our roadmap presents clinical integration as a distinct phase, implementation success requires designing all components with clinical workflows in mind from the outset."

"Agentic AI refers to compound systems in which large language models (LLMs) and other ML models are orchestrated through scaffolding architectures that enable code execution, search, memory, and iterative refinement, fundamentally differing from single-model approaches. While single LLMs have demonstrated impressive performance on medical licensing examinations and diagnostic benchmarks, they are not able to execute diverse tasks in the real world."

The paper identifies several critical limitations of single LLMs that agentic approaches can address:

1. LLMs exhibit 'jagged intelligence' with gaps between strengths and weaknesses; agentic scaffolding can close these gaps through systematic planning, context engineering, and verification.

2. Hallucinations persist despite improvements, particularly when models lack empirical grounding; agentic systems can enforce scientific grounding by retrieving and cross-checking outputs against trusted medical sources.

3. "Single LLMs lack mechanisms for continual learning, causing their knowledge to lag behind evolving medical evidence; agents can access up-to-date external sources such as FDA approvals, clinical trials, and treatment guidelines at inference time."

4. "Limited context windows and sample inefficiency restrict how much clinical information models can use; agentic architectures can address these constraints by integrating massive, compressed external knowledge stored in vector databases and knowledge graphs through retrieval-augmented generation (RAG)."

5. LLMs lack calibrated statistical metrics to quantify uncertainty during prediction, limiting trust; compound systems can integrate traditional machine learning models that provide quantitative, interpretable outputs.

"As the U.S. population ages, the number of people with Alzheimer's disease and related dementias (ADRD) is rapidly increasing, with US cases projected to double by 2060 to 1 million annually. Early detection and precise diagnosis are crucial for proper access to quality care, eligibility for new and emerging disease modifying therapies, and enabling research to advance the field. Unfortunately, more than 50% of dementia diagnoses are delayed until moderate or advanced stages in primary care with greater delays among racial and ethnic minorities."

"There are many challenges with diagnosing neurodegenerative diseases. Patients usually go to their primary care providers (PCPs) first, but most PCPs cite lack of time and confidence with ADRD diagnosis. Referrals to dementia specialists face significant barriers as the demand greatly exceeds the available specialist supply with the gap continuing to widen, and the average wait times projected to exceed 40 months by 2027 with rural areas facing three times longer delays. Beyond access issues, diagnosis is challenging, requiring time-consuming data collection and nuanced interpretation."

"After diagnosis, challenges with management are growing as new therapeutics with intensive requirements for monitoring are emerging. Many PCPs and general neurologists struggle to keep pace with the rapid advances in the field, which includes maintaining awareness of new treatments, diagnostics, and research studies that could potentially benefit their patients and the ADRD field. Furthermore, new care models such as those proposed by Centers for Medicare and Medicaid Services (CMS) demand additional resources that our current workforce is ill-equipped to support."

Data collection should first prioritize what clinicians can interpret and are currently using to diagnose neurodegenerative disease in both the clinic and research settings. The paper examines key data types: the patient's history, physical examination, neuropsychological testing, neuroimaging, and biomarkers.

"Recent advances in LLM capabilities have made comprehensive remote history collection with voice-based conversational agents feasible for the first time. This includes natural conversational interaction that can match or exceed clinician efficiency and expressions of empathy, engage with persons living with dementia, and adapt to various educational backgrounds and languages."

"Clinicians, faced with limited time, struggle to gather and document comprehensive patient histories from distressed patients or their caregivers, leading to redundant interviews across clinicians and potentially delaying diagnosis. Moreover, incomplete documentation restricts the valuable clinical data available for AI-driven decision support systems. Voice-based conversational agents powered by LLMs could transform history collection by enabling these interviews to be conducted over the phone outside time-limited clinic visits, increasing healthcare access for patients who are of lower socioeconomic status, have limited English proficiency, and/or have low digital literacy."

"There are several considerations for the efficacy and safety of interactions between patients and conversational agents. Interviews will need to be semi-structured as LLMs could engage in irrelevant or dangerous topics. Continuous real-time monitoring systems are needed to analyze the conversation for content relevance and for dangerous content such as expressions of suicidal ideation, automatically triggering human intervention. A hybrid model that combines automated conversational agents with timely clinician oversight and decision-making is strongly advocated. Bias will also need to be identified and mitigated (e.g., these tools can have worse performance with non-English speaking people)."

"Most clinicians would not realistically adopt a system requiring them to read a lengthy transcript, but LLM summarization capabilities enable workflow integration by converting these interviews into a history of present illness (HPI). Reliable summarization is especially critical in cognitive impairment contexts, where accurate documentation of subtle symptom changes over time is essential for early diagnosis and management."

"When concerning symptoms are identified, a cognitive assessment with objective measures of cognitive impairment is required. Cognitive assessments should be scalable, without requiring highly trained staff, and designed to identify relative weaknesses of specific cognitive and behavioral domains to enable identification of patterns helpful with diagnosis, going beyond brief screening assessments. Traditional assessments are time-consuming, require specialized training, and often capture only summary scores rather than the detailed, domain-specific performance data valuable for AI interpretation."

"Digital cognitive assessments will be necessary to enable a comprehensive AI system for ADRD, and they address the above limitations by capturing nuances in performance (trial-level data, response times, error patterns) unavailable with paper-based tests. Their ability to integrate directly into the clinician's workflow within the EHR has driven adoption, as shown with the TABCat-BHA. The digital cognitive assessment landscape is advancing rapidly, with clinically available tools continually improving in accuracy, accessibility, and ecological validity. Virtual reality assessments represent one particularly promising direction, with emerging LLM integration enabling virtual psychometrists that could fundamentally transform how cognitive evaluations are conducted."

"Note that gathering the history and cognitive assessment ahead of the visit could free up time with the clinician, enabling a conversation designed to build rapport and shared decision making that a clinician would not otherwise have the time to do well."

"Physical examination continues to be important in obtaining a diagnosis and the motor findings associated with Lewy body disease, corticobasal syndrome, progressive supranuclear palsy, amyotrophic lateral sclerosis, cerebrovascular disease and many other conditions. In addition, the absence of motor findings in AD is also important. While an in-person physical exam is ideal, physical exam findings for these syndromes can often be uncovered through video visits which can increase accessibility for patients who cannot feasibly travel to see a specialist. Possibilities include video interpretation of speech and speech patterns, motor planning and execution, gait, balance, and others. New multimodal AI models have made significant progress in describing video content such as surgeries, making video data a promising method for AI decision support with further research."

"Neuroimaging with MRI and Positron Emission Tomography (PET) is a diagnostic cornerstone, aiding in exclusion of treatable causes of dementia, assessing vascular etiologies, and observing atrophy patterns (MRI) or biomarker location (PET). MRI is becoming more accessible around the country with several creative solutions, so we do not see this as a notable bottleneck for the future of AI decision support."

"Biomarkers are now a valuable component of the diagnostic process. While amyloid PET and cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease have not been easily scalable or accessible, multiple plasma-based biomarkers are becoming available in the clinic setting. Assays of cerebrospinal fluid and more recently skin biopsies are promising for detection of alpha synuclein, which is a biomarker of Parkinsons' disease, Dementia with Lewy bodies, and multiple system atrophy. The implementation and real world accuracy of these biomarkers is highly promising. Experts in the field have emphasized that accurate interpretation of these biomarkers still requires clinical context, which is a concept that can be extended to any data type. Therefore, an AI-system must integrate data across modalities with unique patient characteristics (e.g., age and kidney function significantly alter interpretation of new plasma AD biomarkers) to inform diagnosis and management."

"Standardized data collection is the essent…

Based on the paper, here are the specific improvements I can make to an AI system, along with what the improved system can do:

• Add a modality-to-language standardization layer that converts neuroimaging features (atrophy scores, cortical thinning), biomarker results (plasma p-tau217, Aβ42/40, CSF α-synuclein), and neuropsychological test outputs (trial-level data, response times, error patterns) into structured textual summaries consumable by LLM-based agents.

• Implement retrieval-augmented generation (RAG) with a continuously updated vector database containing FDA approvals, clinical trial eligibility criteria, and treatment guidelines (e.g., lecanemab, donanemab monitoring requirements) to ground all recommendations in the latest evidence.

• Build a multi-agent system with specialized agents for behavioral neurology, neuropsychology, and neuroimaging, each performing independent analysis of the standardized data, then synthesizing outputs through a multidisciplinary team review layer that produces a clinician-readable differential diagnosis with explicit reasoning chains.

• Add a sequential diagnostic reasoning module that surfaces intermediate steps (what was asked, what was observed, why a recommendation follows) for full auditability.

• Implement a semi-structured voice-based conversational agent that conducts patient/caregiver interviews over the phone, with:

• Real-time content monitoring for dangerous topics (suicidal ideation, self-harm) triggering automatic human intervention

• Automatic conversion of interviews into a structured History of Present Illness (HPI) summary

• Adaptation to patient education level, language, and cultural context

• Add LLM-based summarization that preserves subtle symptom changes over time, critical for early diagnosis.

• Combine traditional discriminative models (for well-defined classification tasks like distinguishing AD vs. FTD from imaging) with generative LLM agents (for hypothesis generation and handling diagnostic uncertainty), orchestrated so the discriminative models provide quantitative, interpretable outputs that ground the LLM's reasoning.

• Add explainability layer that translates model inferences into clinical terms (e.g., bilateral hippocampal atrophy pattern consistent with AD, supported by plasma p-tau217 elevation) rather than raw probabilities.

• Implement passive feedback loops where specialists reviewing AI outputs (during e-consults or patient visits) implicitly validate or flag errors, feeding a curated dataset for model updates.

• Add case selection criteria for learning data: only cases with (a) biomarker-confirmed pathology, (b) multiple specialist agreement, or (c) longitudinal diagnostic confirmation are used for model refinement, preventing contaminated updates.

• Integrate real-time literature monitoring that updates the RAG database with human oversight, ensuring the system never lags behind new evidence.

• Add diverse population validation using specialist-confirmed cases from underserved groups (racial/ethnic minorities, limited English proficiency, low digital literacy) to detect and correct performance disparities.

• Implement ongoing fairness dashboards that track model performance stratified by patient demographics, flagging any drift in accuracy for specific subgroups.

• Build SMART on FHIR compatible modules that integrate directly into EHR systems (Epic, Oracle), presenting AI-generated differentials and management recommendations within the clinician's existing workflow.

• Add clinician-AI interaction monitoring to detect over-reliance (clinician accepting AI outputs without review) or underutilization, triggering targeted training interventions.

• Implement redundant safety systems with clear escalation pathways for adverse events, including automatic alerts when AI recommendations conflict with patient safety parameters.

• Use open-source, smaller LLMs (8B-70B parameters) fine-tuned for ADRD-specific tasks, with 4-bit QLoRA quantization for low-latency, on-device inference, reducing infrastructure costs and enabling deployment in resource-limited primary care settings.

• Design the system to support e-consults as the primary use case, enabling specialists to review AI-prepared cases in under 10 minutes, scaling specialist expertise without requiring in-person visits.

1. Conduct comprehensive remote patient interviews via phone, producing structured histories that capture subtle cognitive symptom changes, with automatic safety monitoring and human escalation for crisis situations.

2. Generate a multimodal differential diagnosis by integrating patient history, digital cognitive assessments, neuroimaging (MRI/PET), and blood/CSF biomarkers, presenting the reasoning in familiar clinical terms with explicit evidence chains.

3. Evaluate complex therapy eligibility (e.g., anti-amyloid monoclonal antibodies) by systematically checking inclusion/exclusion criteria against patient data, flagging potential candidates earlier and reducing specialist burden.

4. Provide personalized management recommendations grounded in the latest evidence, including disease-modifying therapies, supportive care (psychotherapy, physical therapy, lifestyle interventions), and clinical trial matching.

5. Learn from every specialist interaction while maintaining rigorous quality gates—only validated, diverse cases influence model updates, preventing performance degradation and bias amplification.

6. Operate in resource-limited settings (primary care, rural clinics) with minimal infrastructure requirements, using open-source models and standardized data collection that doesn't require specialized staff.

7. Continuously monitor its own performance through dashboards tracking accuracy, fairness across demographic groups, and clinician-AI interaction patterns, with automatic alerts for model drift or emerging biases.

8. Support e-consult workflows where specialists can review AI-prepared cases in minutes, reducing wait times from months to days and expanding access to expert-level ADRD care for underserved populations.

9. Protect patient autonomy and safety through redundant monitoring, transparent reasoning, and human-in-the-loop oversight at every critical decision point, with clear accountability frameworks and adverse event reporting mechanisms.