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ARXIV:2603.25322 · HEALTHCARE AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.25322HEALTHCARE AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEWenlong Hou · Sheng Bi · Guangqian Yang · Lihao Liu · Ye Du · Hanxiao Xue · +13 at arXiv
AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration.
Opportunity summary
Pain AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and…
Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather…
Healthcare AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration.
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Paper Pack
10.48550/arXiv.2603.25322AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration.
Abstract
Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather than generating comprehensive diagnostic reports that support clinical decision-making. Here we expand LLM capabilities for clinical decision support by introducing AD-CARE, a modality-agnostic agent that performs guideline-grounded diagnostic assessment from incomplete, heterogeneous inputs without imputing missing modalities. By dynamically orchestrating specialized diagnostic tools and embedding clinical guidelines into LLM-driven reasoning, AD-CARE generates transparent, report-style outputs aligned with real-world clinical workflows. Across six cohorts comprising 10,303 cases, AD-CARE achieved 84.9% diagnostic accuracy, delivering 4.2%-13.7% relative improvements over baseline methods. Despite cohort-level differences, dataset-specific accuracies remain robust (80.4%-98.8%), and the agent consistently outperforms all baselines. AD-CARE reduced performance disparities across racial and age subgroups, decreasing the average dispersion of four metrics by 21%-68% and 28%-51%, respectively. In a controlled reader study, the agent improved neurologist and radiologist accuracy by 6%-11% and more than halved decision time. The framework yielded 2.29%-10.66% absolute gains over eight backbone LLMs and converges their performance. These results show that AD-CARE is a scalable, practically deployable framework that can be integrated into routine clinical workflows for multimodal decision support in AD.
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What was readable
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Dimensions overall score 7.0
PROBLEM
AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability...
METHOD
Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability acro...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather than generating comprehensive diag...
WHY NOW
Healthcare AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather than generating comprehensive diagnostic reports that support clinical decision-making. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Healthcare AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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AD-CARE: An AI-powered, guideline-driven agent for enhancing Alzheimer's diagnosis accuracy and clinical efficiency through modality-agnostic multi-modal data integration.
Segment
Healthcare AI
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reason
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proof status
unverified
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passport absent
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partial
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