Hebert, L. E., Weuve, J., Scherr, P. A. & Evans, D. A. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology 80, 1778–1783 (2013).

Article 
PubMed 
PubMed Central 

Google Scholar
 

GBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 459–480 (2019).

Fang, M. et al. Lifetime risk and projected burden of dementia. Nat. Med. https://doi.org/10.1038/s41591-024-03340-9 (2025).

Bradford, A., Kunik, M. E., Schulz, P., Williams, S. P. & Singh, H. Missed and delayed diagnosis of dementia in primary care: prevalence and contributing factors. Alzheimer Dis. Assoc. Disord. 23, 306–314 (2009).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Kotagal, V. et al. Factors associated with cognitive evaluations in the United States. Neurology 84, 64–71 (2015).

Article 
PubMed 

Google Scholar
 

Babulal, G. M. et al. Perspectives on ethnic and racial disparities in Alzheimer’s disease and related dementias: update and areas of immediate need. Alzheimers Dement. 15, 292–312 (2019).

Article 
PubMed 

Google Scholar
 

Boustani, M. et al. Implementing a screening and diagnosis program for dementia in primary care. J. Gen. Intern. Med. 20, 572–577 (2005).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Bernstein, A. et al. Primary care provider attitudes and practices evaluating and managing patients with neurocognitive disorders. J. Gen. Intern. Med. 34, 1691–1692 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Majersik, J. J. et al. A shortage of neurologists – we must act now: a report from the AAN 2019 Transforming Leaders Program. Neurology 96, 1122–1134 (2021).

Article 
PubMed 

Google Scholar
 

Satiani, A., Niedermier, J., Satiani, B. & Svendsen, D. P. Projected workforce of psychiatrists in the United States: a population analysis. Psychiatr. Serv. 69, 710–713 (2018).

Article 
PubMed 

Google Scholar
 

Flaherty, E. & Bartels, S. J. Addressing the community-based geriatric healthcare workforce shortage by leveraging the potential of interprofessional teams. J. Am. Geriatr. Soc. 67, S400–S408 (2019).

Article 
PubMed 

Google Scholar
 

Liu, J. L. et al. Modeling early detection and geographic variation in health system capacity for Alzheimer’s disease-modifying therapies. RAND Corp. https://doi.org/10.7249/RRA2643-1 (2024).

van Dyck, C. H. et al. Lecanemab in early Alzheimer’s disease. N. Engl. J. Med. 388, 9–21 (2023).

Article 
PubMed 

Google Scholar
 

Sims, J. R. et al. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical trial. JAMA 330, 512–527 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Guiding an Improved Dementia Experience (GUIDE) Model. CMS https://www.cms.gov/priorities/innovation/innovation-models/guide/ (accessed 18 April 2024).

Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Singhal, K. et al. Toward expert-level medical question answering with large language models. Nat. Med. https://doi.org/10.1038/s41591-024-03423-7 (2025).

Tu, T. et al. Towards generalist biomedical AI. NEJM AI 1, AIoa2300138 (2024).

Article 

Google Scholar
 

Tu, T. et al. Towards conversational diagnostic artificial intelligence. Nature https://doi.org/10.1038/s41586-025-08866-7 (2025).

Liu X., et al. A generalist medical language model for disease diagnosis assistance. Nat. Med. https://doi.org/10.1038/s41591-024-03416-6 (2025).

American Medical Association Center for Digital Health and AI. 2026 Physician Survey on Augmented Intelligence 2026. ama-assn.org https://www.ama-assn.org/system/files/physician-ai-sentiment-report.pdf (2026).

Gupta, R. et al. The shift from models to compound AI systems. The Berkeley Artificial Intelligence Research Blog https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/ (2024).

Acharya, D. B., Kuppan, K. & Divya, B. Agentic AI: autonomous intelligence for complex goals—a comprehensive survey. IEEE Access 13, 18912–18936 (2025).

Article 

Google Scholar
 

For trustworthy AI, keep the human in the loop. Nat Med. 31, 3207 (2025).

Sheehy, L. et al. Development and initial testing of an artificial intelligence-based virtual reality companion for people living with dementia in long-term care. J. Clin. Med. 13, 5574 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Ray, M. et al. Evaluating a large language model in translating patient instructions to spanish using a standardized framework. JAMA Pediatr. 179, 1026–1033 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Tong, L. et al. Telemedicine and health disparities: Association between patient characteristics and telemedicine, in-person, telephone and message-based care during the COVID-19 pandemic. IPEM Transl. 3, 100010 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Nota, S. P. F. T., Strooker, J. A. & Ring, D. Differences in response rates between mail, e-mail, and telephone follow-up in hand surgery research. Hand 9, 504–510 (2014).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Pagán, V. M., McClung, K. S. & Peden, C. J. An observational study of disparities in telemedicine utilization in primary care patients before and during the COVID-19 pandemic. Telemed. J. E Health 28, 1117–1125 (2022).

Article 
PubMed 

Google Scholar
 

Hinton, L. et al. Practice constraints, behavioral problems, and dementia care: primary care physicians’ perspectives. J. Gen. Intern. Med. 22, 1487–1492 (2007).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Qu, Y. & Wang, J. Performance and biases of large language models in public opinion simulation. Humanit. Soc. Sci. Commun. 11, 1–13 (2024).

Article 

Google Scholar
 

Van Veen, D. et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat. Med. 30, 1134–1142 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Breithaupt, A. G. et al. Enhancing early detection of cognitive impairment in primary care with the TabCAT-BHA. Alzheimers Dement. 21, e70437 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Chen, L., Zhen, W. & Peng, D. Research on digital tool in cognitive assessment: a bibliometric analysis. Front. Psychiatry https://doi.org/10.3389/fpsyt.2023.1227261 (2023).

Belleville, S., LaPlume, A. A. & Purkart, R. Web-based cognitive assessment in older adults: where do we stand?. Curr. Opin. Neurol. 36, 491–497 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Mancuso, V. et al. Systematic review of memory assessment in virtual reality: evaluating convergent and divergent validity with traditional neuropsychological measures. Front. Hum. Neurosci. https://doi.org/10.3389/fnhum.2024.1380575 (2024).

Breithaupt, A. G. et al. Review of artificial intelligence for clinical use in Alzheimer’s disease and related dementias. Semin. Neurol. https://doi.org/10.1055/a-2744-9871 (2025).

Adnan, T. et al. AI-enabled Parkinson’s disease screening using smile videos. NEJM AI 2, AIoa2400950 (2025).

Article 

Google Scholar
 

Deng, D. et al. Interpretable video-based tracking and quantification of parkinsonism clinical motor states. NPJ Parkinsons Dis. 10, 122 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Zhang, K. et al. Recent advances in compact portable platforms and gradient hardware for brain MRI. Radiology 315, e241904 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Ashton, N. J. et al. Diagnostic accuracy of a plasma phosphorylated tau 217 immunoassay for Alzheimer disease pathology. JAMA Neurol. 81, 255–263 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Janelidze, S. et al. Plasma phosphorylated Tau 217 and Aβ42/40 to predict early brain Aβ accumulation in people without cognitive impairment. JAMA Neurol. 81, 947–957 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Palmqvist, S. et al. Blood biomarkers to detect Alzheimer disease in primary care and secondary care. JAMA https://doi.org/10.1001/jama.2024.13855 (2024).

Arnold, M. R. et al. α-synuclein seed amplification in CSF and brain from patients with different brain distributions of pathological α-synuclein in the context of co-pathology and non-LBD diagnoses. Ann. Neurol. 92, 650–662 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Gibbons, C. H. et al. Skin biopsy detection of phosphorylated α-synuclein in patients with synucleinopathies. JAMA 331, 1298–1306 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Samudra, N. et al. Clinicopathological correlation of cerebrospinal fluid alpha-synuclein seed amplification assay in a behavioral neurology autopsy cohort. Alzheimers Dement. 20, 3334–3341 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Bouwman, F. H. et al. Clinical application of CSF biomarkers for Alzheimer’s disease: from rationale to ratios. Alzheimers Dement. 14, e12314 (2022).


Google Scholar
 

Dubois, B. et al. Alzheimer disease as a clinical-biological construct—an International Working Group recommendation. JAMA Neurol. https://doi.org/10.1001/jamaneurol.2024.3770 (2024).

Jasodanand, V. H., Bellitti, M. & Kolachalama, V. B. An AI-first framework for multimodal data in Alzheimer’s disease and related dementias. Alzheimers Dement. 21, e70719 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Wilkinson, M. D. et al. The FAIR Guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Shah, N. H. et al. A nationwide network of health ai assurance laboratories. JAMA 331, 245–249 (2024).

Article 
PubMed 

Google Scholar
 

HL7 International. FHIR R5: fast healthcare interoperability resources. hl7.org https://www.hl7.org/fhir/index.html (2023).

SMART Health IT. smarthealthit.org https://smarthealthit.org/ (accessed 25 October 2025).

Oracle Health. Oracle Health Millennium Platform APIs. oracle.com https://docs.oracle.com/en/industries/health/millennium-platform-apis/index.html (2025).

Epic Systems. Epic’s home for data sharing and developer tools. epic.com https://open.epic.com/ (2025).

Brown, E. G. et al. Enhancing clinical information display to improve patient encounters: human-centered design and evaluation of the Parkinson Disease-BRIDGE platform. JMIR Hum. Factors 9, e33967 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Park, W. Y. et al. Breaking data silos: incorporating the DICOM imaging standard into the OMOP CDM to enable multimodal research. J. Am. Med. Inform. Assoc. 32, 1533–1541 (2025).

PubMed 
PubMed Central 

Google Scholar
 

Lau-Min, K. S. et al. Impact of integrating genomic data into the electronic health record on genetics care delivery. Genet. Med. 24, 2338–2350 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Hoffman, S. L. et al. Comprehensive real time remote monitoring for Parkinson’s disease using quantitative DigitoGraphy. NPJ Parkinsons Dis. 10, 137 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

McHugh, C. P., Clement, M. H. S. & Phatak, M. AD Workbench: transforming Alzheimer’s research with secure, global, and collaborative data sharing and analysis. Alzheimers Dement. 21, e70278 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Brat, G. A., Mandel, J. C. & McDermott, M. B. A. Do we need data standards in the era of large language models?. NEJM AI 1, AIe2400548 (2024).

Article 

Google Scholar
 

Savage, T., Nayak, A., Gallo, R., Rangan, E. & Chen, J. H. Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine. NPJ Digit. Med. 7, 20 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019).

Article 
PubMed 

Google Scholar
 

Bron, E. E. et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111, 562–579 (2015).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Xue, C. et al. AI-based differential diagnosis of dementia etiologies on multimodal data. Nat. Med. 30, 2977–2989 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Rauschecker, A. M. et al. Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI. Radiology 295, 626–637 (2020).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Leming, M. & Im, H. Differential dementia detection from multimodal brain images in a real-world dataset. Alzheimers Dement. 21, e70362 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Qiu, S. et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat. Commun. 13, 3404 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Jasodanand, V. H. et al. AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment. Nat. Commun. 16, 7407 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Tanabe, J. et al. Automated volumetric software in dementia: help or hindrance to the neuroradiologist?. AJNR Am. J. Neuroradiol. 45, 1737–1744 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Haller, S. et al. Basic MR sequence parameters systematically bias automated brain volume estimation. Neuroradiology 58, 1153–1160 (2016).

Article 
PubMed 

Google Scholar
 

Wittens, M. M. J. et al. Diagnostic performance of automated MRI volumetry by icobrain dm for Alzheimer’s disease in a clinical setting: a REMEMBER study. J. Alzheimers Dis. 83, 623–639 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Yu, K. H., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 719–731 (2018).

Article 
PubMed 

Google Scholar
 

Aristidou, A., Jena, R. & Topol, E. J. Bridging the chasm between AI and clinical implementation. Lancet 399, 620 (2022).

Article 
PubMed 

Google Scholar
 

Schubert, M. C., Wick, W. & Venkataramani, V. Performance of large language models on a neurology board–style examination. JAMA Netw. Open 6, e2346721 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Koga, S., Martin, N. B. & Dickson, D. W. Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders. Brain Pathol. 34, e13207 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Ayers, J. W. et al. Comparing Physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern. Med. 183, 589–596 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Gallingani, C. et al. Agentic generative artificial intelligence system for classification of pathology-confirmed primary progressive aphasia variants. Preprint at medRxiv https://doi.org/10.1101/2025.10.28.25338977 (2025).

Saab, K., Park, C. & Strother, T. et al. Advancing conversational diagnostic AI with multimodal reasoning. Nat. Med. 32, 1726–1736 (2026).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Saab, K. et al. Advancing conversational diagnostic AI with multimodal reasoning. Preprint at https://arxiv.org/abs/2505.04653 (2025).

El Arab, R. A. & Al Moosa, O. A. Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare. NPJ Digit. Med. 8, 548 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Aye, S., Handels, R., Winblad, B. & Jönsson, L. Optimising Alzheimer’s disease diagnosis and treatment: assessing cost-utility of integrating blood biomarkers in clinical practice for disease-modifying treatment. J. Prev. Alzheimers Dis. 11, 928–942 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Bharadwaj, P. et al. Unlocking the value: quantifying the return on investment of hospital artificial intelligence. J. Am. Coll. Radiol. 21, 1677–1685 (2024).

Article 
PubMed 

Google Scholar
 

Stanford HAI. The 2025 AI Index Report. hai.stanford.edu https://hai.stanford.edu/ai-index/2025-ai-index-report/ (accessed 11 October 2025).

Parikh, R. B. & Helmchen, L. A. Paying for artificial intelligence in medicine. NPJ Digit. Med. 5, 63 (2022).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Peterson Health Technology Institute. Adoption of artificial intelligence in healthcare delivery systems: early applications and impacts. phti.org https://phti.org/ai-adoption-early-applications-impacts/ (2025).

Tardo, L. et al. Evaluation of the impact of neurology electronic consults (e-consults): experiences of a neurology resident clinic in a safety-net hospital. J. Telemed. Telecare 31, 1270–1277 (2025).

Article 
PubMed 

Google Scholar
 

Bedi, S. et al. Testing and evaluation of health care applications of large language models: a systematic review. JAMA https://doi.org/10.1001/jama.2024.21700 (2024).

The Federal Register/FIND. Health data, technology, and interoperability: certification program updates, algorithm transparency, and information sharing. Vol 89, 1192. proquest.com https://www.proquest.com/docview/2912085944/citation/14F0AB28E9DD4318PQ/1 (2024).

Goh, E. et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw. Open 7, e2440969 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Jabbour, S. et al. Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study. JAMA 330, 2275–2284 (2023).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Tonmoy, S. M. et al. A comprehensive survey of hallucination mitigation techniques in large language models. Preprint at http://arxiv.org/abs/2401.01313 (2024).

Bucher, M. J. J. & Martini, M. Fine-tuned ‘small’ LLMs (still) significantly outperform zero-shot generative ai models in text classification. Preprint at https://arxiv.org/html/2406.08660v1 (2024).

Hugging Face. Open Medical-LLM Leaderboard – a Hugging Face Space by openlifescienceai. huggingface.co https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard/ (accessed 13 November 2024).

Dettmers, T., Pagnoni, A., Holtzman, A. & Zettlemoyer, L. QLORA: efficient finetuning of quantized LLMs. In Proc. 37th Int. Conf. on Neural Information Processing Systems. NIPS ’23 (eds. Oh, A. et al.) 10088–10115 (Curran Associates Inc., 2023).

Abadir, P. M., Battle, A., Walston, J. D. & Chellappa, R. Enhancing care for older adults and dementia patients with large language models: proceedings of the National Institute on Aging—Artificial Intelligence & Technology Collaboratory for Aging Research Symposium. J. Gerontol. A Biol. Sci. Med. Sci. 79, glae176 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).

Article 
PubMed 

Google Scholar
 

Leslie, D., Mazumder, A., Peppin, A., Wolters, M. K. & Hagerty, A. Does ‘AI’ stand for augmenting inequality in the era of COVID-19 healthcare?. BMJ 372, n304 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Savulescu, J., Giubilini, A., Vandersluis, R. & Mishra, A. Ethics of artificial intelligence in medicine. Singapore Med. J. 65, 150–158 (2024).

Article 
PubMed 
PubMed Central 

Google Scholar
 

US Department of Health and Human Services. IRB considerations on the use of artificial intelligence in human subjects research. hhs.gov https://www.hhs.gov/ohrp/sachrp-committee/recommendations/irb-considerations-use-artificial-intelligence-human-subjects-research/index.html (2022).

Sarkar AR, Chuang YS, Mohammed N, Jiang X. De-identification is not enough: a comparison between de-identified and synthetic clinical notes. Sci Rep. 2024;14(1):29669. https://doi.org/10.1038/s41598-024-81170-y

Broniatowski, D. A. Psychological foundations of explainability and interpretability in artificial intelligence. National Institute of Standards and Technology (US); NIST IR 8367. NIST https://doi.org/10.6028/NIST.IR.8367 (2021).

Tabassi, E. Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (US); NIST AI 100-1. NIST https://doi.org/10.6028/NIST.AI.100-1 (2023).

Phillips, P. J. et al. Four principles of explainable artificial intelligence. National Institute of Standards and Technology (US); NIST IR 8312. NIST https://doi.org/10.6028/NIST.IR.8312 (2021).

Sim, I. & Cassel, C. The ethics of relational AI — expanding and implementing the Belmont principles. N. Engl. J. Med. 391, 193–196 (2024).

Article 
PubMed 

Google Scholar
 

Mello, M. M. & Cohen, I. G. Regulation of health and health care artificial intelligence. JAMA 333, 1769–1770 (2025).

Article 
PubMed 

Google Scholar
 

Dell’Acqua, F. et al. Navigating the jagged technological frontier: field experimental evidence of the effects of AI on knowledge worker productivity and quality. SSRN Electron J. https://doi.org/10.2139/ssrn.4573321 (2023).

Bedi, S., Fries, J. A. & Shah, N. H. How to interpret ‘zero-shot’ results from generative EHR models. Nat. Med. 32, 404–406 (2026).

Article 
PubMed 

Google Scholar
 

Shi, H. et al. Continual learning of large language models: a comprehensive survey. ACM Comput. Surv. 58, 120:1–120:42 (2025).


Google Scholar
 

Liu, N. F. et al. Lost in the middle: how language models use long contexts. Trans. Assoc. Comput. Linguist. 12, 157–173 (2024).

Article 

Google Scholar
 

Gargari, O. K. & Habibi, G. Enhancing medical AI with retrieval-augmented generation: a mini narrative review. Digit. Health https://doi.org/10.1177/20552076251337177 (2025).

He, J. et al. Retrieval-augmented generation in biomedicine: a survey of technologies, datasets, and clinical applications. Preprint at https://arxiv.org/abs/2505.01146 (2025).

Lopopolo, R. Harness engineering: leveraging Codex in an agent-first world. OpenAI https://openai.com/index/harness-engineering/ (accessed 27 April 2026).

Lu, C. et al. Towards end-to-end automation of AI research. Nature 651, 914–919 (2026).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Model Context Protocol. What is the Model Context Protocol (MCP)? modelcontextprotocol.io https://modelcontextprotocol.io/docs/getting-started/intro/ (accessed 27 April 2026).