Towards an AI biomedical scientist: Accelerating discoveries in neurodegenerative disease
Open accessAbstract
Despite major advances in Alzheimer's disease and related diseases (ADRD) research, the translation of discoveries into impactful clinical interventions remains slow. Overwhelming data complexity, fragmented knowledge, and prolonged research cycles hinder progress in understanding and treating neurodegenerative diseases. Artificial intelligence (AI) offers a promising path forward, particularly when developed as a scientist-in-the-loop system that collaborates with researchers throughout the scientific discovery process. This paper introduces the concept of an AI Biomedical Scientist, an intelligent platform designed to support literature synthesis, hypothesis generation, experimental design, and data interpretation. This platform aims to function as a holistic scientific partner, integrating diverse biomedical data and expert reasoning to accelerate discovery. We review commercial and academic efforts and introduce targeted Minimum Viable Products (MVPs) needed for general biomedical research lab utilization of AI, such as robust and accurate tools for literature and data analysis, negative data models, and virtual peer review, with a longer-term vision of foundation models trained directly on biomedical datasets. In AD and neurodegeneration research, such tools are anticipated to deliver efficiency gains ranging from modest improvements in specific research tasks to potential multi-fold accelerations in discovery workflows as systems mature and scale. This review examines the technical foundations, challenges, and anticipated impacts of AI and aims to inform and engage researchers in utilizing these systems to transform biomedical discovery, starting with AD and extending to other complex conditions.
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New LLM-based tools supporting end-to-end scientific workflows are emerging.
“new LLM-based tools supporting end-to-end scientific workflows are emerging 34 .”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [34]
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · cited 1× · see citing works
“Responsible development of AI tools for biomedical research depends on maintaining high scientific standards [108].”
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
FinnGen provides genetic insights from a well-phenotyped isolated population
Kurki, M. I., Karjalainen, J., Palta, P., Sipilä, T. P., Kristiansson, K., Donner, K. M., Reeve, M. P., Laivuori, H., Aavikko, M., Kaunisto, M. A., et al. (2023) · Nature · doi:10.1038/s41586-022-05473-8 · Cited by 4,233 · see citing works
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial
Sims, J. R., Zimmer, J. A., Evans, C. D., Lu, M., Ardayfio, P., Sparks, J., Wessels, A. M., Shcherbinin, S., Wang, H., Monkul Nery, E. S., et al. (2023) · JAMA · doi:10.1001/jama.2023.13239 · Cited by 2,932 · see citing works
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · cited 1× · see citing works
“Artificial intelligence (AI) has the potential to improve how researchers navigate and interpret complex biomedical data [16 – 20].”
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Privacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522 · see citing works
Negative results are disappearing from most disciplines and countries
Fanelli, D. (2012) · Scientometrics · doi:10.1007/s11192-011-0494-7 · Cited by 1,248 · see citing works
BioGPT: generative pre-trained transformer for biomedical text generation and mining
Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., & Liu, T.-Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac409 · Cited by 1,150 · see citing works
Ellis, K. A., Bush, A. I., Darby, D., Fazio, D. D., Foster, J. K., Hudson, P. J., Lautenschlager, N. T., Lenzo, N. P., Martins, R. N., Maruff, P. T., et al. (2009) · International Psychogeriatrics · doi:10.1017/s1041610209009405 · Cited by 970 · see citing works
The National Alzheimer's Coordinating Center (NACC) Database: The Uniform Data Set
Beekly, D. L., Ramos, E. M., Lee, W. W., Deitrich, W. D., Jacka, M. E., Wu, J., Hubbard, J. L., Koepsell, T. D., Morris, J. C., & Kukull, W. A. (2007) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e318142774e · Cited by 907 · see citing works
Papers and patents are becoming less disruptive over time
Park, M., Leahey, E., & Funk, R. J. (2023) · Nature · doi:10.1038/s41586-022-05543-x · Cited by 899 · see citing works
A global federated real-world data and analytics platform for research
Palchuk, M. B., London, J. W., Perez-Rey, D., Drebert, Z. J., Winer-Jones, J. P., Thompson, C. N., Esposito, J., & Claerhout, B. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad035 · Cited by 831 · see citing works
Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717 · see citing works
Cam-CAN, Shafto, M. A., TYLER, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., Calder, A. J., Marslen‐Wilson, W. D., Duncan, J., et al. (2014) · BMC Neurology · doi:10.1186/s12883-014-0204-1 · Cited by 684 · see citing works
Why Most Clinical Research Is Not Useful
Ioannidis, J. P. A. (2016) · PLOS Medicine · doi:10.1371/journal.pmed.1002049 · Cited by 655 · see citing works
The mPower study, Parkinson disease mobile data collected using ResearchKit
Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.11 · Cited by 630 · see citing works
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., & Ferraiuolo, L. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0377-8 · Cited by 595 · see citing works
Barthélemy, N. R., Salvadó, G., Schindler, S. E., He, Y., Janelidze, S., Collij, L. E., Saef, B., Henson, R. L., Chen, C. D., Gordon, B. A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02869-z · Cited by 448 · cited 1× · see citing works
“More than a century after its initial description by Alois Alz-heimer, research has advanced significantly, leading to the development of highly accurate diagnostic biomarkers and modestly effective disease-modifying therapies [1 – 3].”
Self-citationThe Lifespan Human Connectome Project in Aging: An overview
Bookheimer, S. Y., Salat, D. H., Terpstra, M., Ances, B. M., Barch, D. M., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Díaz‐Santos, M., Elam, J. S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.009 · Cited by 440 · see citing works
Trial of solanezumab in preclinical Alzheimer's disease
Sperling, R. A., Donohue, M. C., Raman, R., Rafii, M. S., Johnson, K., Masters, C. L., van Dyck, C. H., Iwatsubo, T., Marshall, G. A., Yaari, R., et al. (2023) · New England Journal of Medicine · doi:10.1056/nejmoa2305032 · Cited by 429 · see citing works
The strain on scientific publishing
Hanson, M. A., Barreiro, P. G., Crosetto, P., & Brockington, D. (2024) · Quantitative Science Studies · doi:10.1162/qss_a_00327 · Cited by 345 · see citing works
Cutillo, C. M., Sharma, K. R., Foschini, L., Kundu, S., Mackintosh, M., Mandl, K. D., MI in Healthcare Workshop Working Group, Beck, T., Collier, E., Colvis, C., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0254-2 · Cited by 337 · see citing works
Self-citationYiannopoulou, K. G., Anastasiou, A. I., Zachariou, V., & Pelidou, S.-H. (2019) · Biomedicines · doi:10.3390/biomedicines7040097 · Cited by 330 · see citing works
Empowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315 · see citing works
The AHEAD 3‐45 Study: Design of a prevention trial for Alzheimer's disease
Rafii, M. S., Sperling, R. A., Donohue, M. C., Zhou, J., Roberts, C., Irizarry, M. C., Dhadda, S., Sethuraman, G., Kramer, L. D., Swanson, C. J., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12748 · Cited by 291 · see citing works
Liang, W., Zhang, Y., Cao, H., Wang, B., Ding, D., Yang, X., Vodrahalli, K., He, S., Smith, D., Yin, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.01783 · Cited by 243 · cited 1× · see citing works
“Initial evaluations of LLMs as scientific reviewers have demonstrated substantial overlap between human and AI generated reviews and overall positive user perceptions of usefulness [98].”
Jessen, F., Spottke, A. E., Boecker, H., Brosseron, F., Büerger, K., Catak, C., Fließbach, K., Franke, C., Fuentes, M., Heneka, M. T., et al. (2018) · Alzheimer s Research & Therapy · doi:10.1186/s13195-017-0314-2 · Cited by 233 · see citing works
Privacy-first health research with federated learning
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00489-2 · Cited by 226 · see citing works
Autonomous biomedical research with an artificial intelligence agent
Huang, K., Zhang, S., Wang, H., Qu, Y., Lu, Y., Li, R., Roohani, Y., Qiu, L., Cao, S., Li, G. L., et al. (2026) · Science · doi:10.1126/science.adz4351 · Cited by 198 · see citing works
The Alzheimer's Disease Sequencing Project: Study design and sample selection
Beecham, G. W., Bis, J. C., Martin, E. R., Choi, S.-H., DeStefano, A. L., van Duijn, C. M., Fornage, M., Gabriel, S. B., Koboldt, D. C., Larson, D. E., et al. (2017) · Neurology Genetics · doi:10.1212/nxg.0000000000000194 · Cited by 195 · see citing works
McMurry, J. A., Juty, N., Blomberg, N., Burdett, T., Conlin, T., Conte, N., Courtot, M., Deck, J., Dumontier, M., Fellows, D. K., et al. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2001414 · Cited by 139 · see citing works
Binette, A. P., Gaiteri, C., Wennström, M., Kumar, A., Hristovska, I., Spotorno, N., Salvadó, G., Strandberg, O. T., Mathys, H., Tsai, L., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01737-w · Cited by 130 · see citing works
Biomni: A general-purpose biomedical ai agent
Huang, K., Zhang, S., Wang, H., Qu, Y., Lu, Y., Roohani, Y., Li, R., Qiu, L., Li, G., Zhang, J., et al. (2025) · doi:10.1101/2025.05.30.656746 · Cited by 125 · cited 1× · see citing works
“…systems can help overcome this limitation by surfacing relevant knowledge from across vast datasets and literature corpora, organizing connections, and enabling hypothesis generation that is informed by a broader and more comprehensive information space than a human could synthesize alone [21,22].”
Veitch, D. P., Weiner, M. W., Miller, M., Aisen, P. S., Ashford, M. A., Beckett, L. A., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13449 · Cited by 113 · see citing works
The AD Knowledge Portal: A Repository for Multi‐Omic Data on Alzheimer's Disease and Aging
Greenwood, A. K., Montgomery, K. S., Kauer, N., Woo, K. H., Leanza, Z. J., Poehlman, W. L., Gockley, J., Sieberts, S. K., Bradic, L., Logsdon, B. A., et al. (2020) · Current Protocols in Human Genetics · doi:10.1002/cphg.105 · Cited by 102 · see citing works
The 100-plus Study of cognitively healthy centenarians: rationale, design and cohort description
Holstege, H., Beker, N., Dijkstra, T., Pieterse, K., Wemmenhove, E., Schouten, K., Thiessens, L., Horsten, D., Rechtuijt, S., Sikkes, S., et al. (2018) · bioRxiv · doi:10.1007/s10654-018-0451-3 · Cited by 86 · see citing works
Timing of Biomarker Changes in Sporadic Alzheimer's Disease in Estimated Years from Symptom Onset
Li, Y., Yen, D., Hendrix, R. D., Gordon, B. A., Dlamini, S. L., Barthélemy, N. R., Aschenbrenner, A. J., Henson, R. L., Herries, E. M., Volluz, K., et al. (2024) · Annals of Neurology · doi:10.1002/ana.26891 · Cited by 86 · see citing works
Self-citationThe All of Us Research Program is an opportunity to enhance the diversity of US biomedical research
Bianchi, D. W., Brennan, P. F., Chiang, M. F., Criswell, L. A., D’Souza, R. N., Gibbons, G. H., Gilman, J. K., Gordon, J. A., Green, E. D., Gregurick, S. K., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02744-3 · Cited by 77 · see citing works
Bateman, R. J., Li, Y., McDade, E., Llibre‐Guerra, J. J., Clifford, D. B., Atri, A., Mills, S. L., Santacruz, A. M., Wang, G., Supnet, C. B., et al. (2024) · The Lancet Neurology · doi:10.1016/s1474-4422(25)00024-9 · Cited by 69 · see citing works
Self-citationThe Image and Data Archive at the Laboratory of Neuro Imaging
Crawford, K. L., Neu, S., & Toga, A. W. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.067 · Cited by 67 · see citing works
”My AI is Lying to Me”: User-reported LLM hallucinations in AI mobile apps reviews
Massenon, R., Gambo, I., Khan, J. A., Agbonkhese, C., & Alwadain, A. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-15416-8 · Cited by 66 · see citing works
Artificial Intelligence: The Milestone in Modern Biomedical Research
Athanasopoulou, K., Daneva, G. N., Adamopoulos, P. G., & Scorilas, A. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2040049 · Cited by 65 · see citing works
Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63 · see citing works
Editorial: Journal of Negative Results in Biomedicine
Pfeffer, C., & Olsen, B. R. (2002) · Journal of Negative Results in BioMedicine · doi:10.1186/1477-5751-1-2 · Cited by 62 · see citing works
From intention to implementation: automating biomedical research via LLMs
Luo, Y., Shi, L., Li, Y., Zhuang, A., Gong, Y., Liu, L., & Chen, L. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4485-0 · Cited by 56 · see citing works
From Intention To Implementation: Automating Biomedical Research via LLMs
Luo, Y., Shi, L., Li, Y., Zhuang, A., Gong, Y., Liu, L., & Lin, C. (2024) · arXiv · doi:10.48550/arxiv.2412.09429 · Cited by 53
Literature analysis of artificial intelligence in biomedicine
Hulsen, T. (2022) · Annals of Translational Medicine · doi:10.21037/atm-2022-50 · Cited by 46 · see citing works
The ROSMAP project: aging and neurodegenerative diseases through omic sciences
Pérez-González, A. P., García-Kroepfly, A. L., Pérez-Fuentes, K. A., García-Reyes, R. I., Solis-Roldan, F. F., Alba-González, J. A., Lemus, E. H., & de Anda‐Jáuregui, G. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1443865 · Cited by 44 · see citing works
Biases in AI: acknowledging and addressing the inevitable ethical issues
Hofmann, B. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1614105 · Cited by 44 · see citing works
Fernández, M. V., Liu, M., Beric, A., Johnson, M., Cetin, A., Patel, M. R., Budde, J., Kohlfeld, P., Bergmann, K., Lowery, J., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03485-9 · Cited by 41 · see citing works
Self-citationWagemann, O., Liu, H., Wang, G., Shi, X., Bittner, T., Scelsi, M. A., Farlow, M. R., Clifford, D. B., Supnet, C. B., Santacruz, A. M., et al. (2024) · JAMA Neurology · doi:10.1001/jamaneurol.2024.0991 · Cited by 39 · see citing works
Self-citationAlzheimer's Disease Sequencing Project release 4 whole genome sequencing dataset
Leung, Y. Y., Lee, W., Kuzma, A. B., Nicaretta, H. I., Valladares, O., Gangadharan, P., Qu, L., Zhao, Y., Ren, Y., Cheng, P., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70237 · Cited by 38 · see citing works
Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers
Ifargan, T., Hafner, L., Kern, M., Alcalay, O., & Kishony, R. (2025) · NEJM AI · doi:10.1056/aioa2400555 · Cited by 34 · see citing works
TIHM: An open dataset for remote healthcare monitoring in dementia
Palermo, F., Chen, Y., Capstick, A., Fletcher‐Lloyd, N., Walsh, C., Kouchaki, S., True, J., Balazikova, O., Soreq, E., Scott, G., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02519-y · Cited by 32 · see citing works
Self-citationPublishing negative results is good for science
Bik, E. M. (2024) · Access Microbiology · doi:10.1099/acmi.0.000792 · Cited by 27 · see citing works
Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results
Brazil, R. (2024) · Nature · doi:10.1038/d41586-024-01389-7 · Cited by 21 · see citing works
Lentzen, M., Vairavan, S., Muurling, M., Alepopoulos, V., Atreya, A., Boada, M., de Boer, C., Conde, P., Curcic, J., Frisoni, G. B., et al. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01675-0 · Cited by 21 · see citing works
Self-citationSemNet: Using Local Features to Navigate the Biomedical Concept Graph
Sedler, A. R., & Mitchell, C. S. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00156 · Cited by 18 · see citing works
Self-citationDataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform
Avraam, D., Wilson, B., Chan, N. A., Banerjee, S., Bishop, T. R. P., Butters, O. W., Cadman, T., Cederkvist, L., Duijts, L., Escribà-Montagut, X., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf046 · Cited by 17 · see citing works
McHugh, C., Clement, M. H. S., & Phatak, M. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70278 · Cited by 17 · see citing works
Self-citationTomczyk, P., Brüggemann, P., Mergner, N., & Petrescu, M. (2026) · Journal of Librarianship and Information Science · doi:10.1177/09610006241295802 · Cited by 17 · see citing works
“Real‐world” eligibility for anti‐amyloid treatment in a tertiary memory clinic setting
Vigneswaran, S., Vijverberg, E. G. B., Barkhof, F., van de Giessen, E., Lemstra, A. W., Pijnenburg, Y. A. L., Teunissen, C. E., van der Flier, W. M., & van Harten, A. C. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70375 · Cited by 16 · see citing works
Huang, X., Han, X., Chang, H., Yu, T., Dong, Y., Mao, M., Ding, J., Li, X., Liu, H., Tian, M., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03166-y · Cited by 11 · see citing works
Escribà-Montagut, X., Marcon, Y., Anguita‐Ruiz, A., Avraam, D., Urquiza, J., Morgan, A. S., Wilson, B., Burton, P. R., & González, J. R. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012626 · Cited by 10 · see citing works
Harnessing artificial intelligence to transform Alzheimer’s disease research
Andrieu, S., Bateman, R. J., Bereczki, E., Bose, N., Brookes, A. J., Doraiswamy, P. M., Kivipelto, M., Kohlhaas, S. L., Kourtis, L. C., Middleton, L. T., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03632-8 · Cited by 8 · see citing works
Self-citationKearns, W. G., Stamoulis, G., Glick, J., Baisch, L., Benner, A. T., Brough, D. E., Du, L., Wilson, B., Kearns, L., Ng, N., et al. (2024) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2024.03.004 · Cited by 6 · see citing works
Lovestone, S., & Imam, F. B. (2025) · Nature Aging · doi:10.1038/s43587-025-00920-3 · Cited by 3 · see citing works
15 years of longitudinal genetic, clinical, cognitive, imaging, and biochemical measures in DIAN
Daniels, A. J., McDade, E., Llibre-Guerra, J., Xiong, C.-J., Perrin, R., Ibanez, L., Supnet-Bell, C., Cruchaga, C., Goate, A., Renton, A. E., et al. (2026) · npj Dementia · doi:10.1038/s44400-025-00047-7 · Cited by 2 · see citing works
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Flagged first, then by citations
Research works printed but not linked: 36
- [17]Tu T. Fang Z. Cheng Z. Spasic S. Palepu A. Stankovic K.M. Genetic discovery enabled by A large language model [Internet] bioRxiv https://www.biorxiv.org/content/10.1101/2023.11.09.566468v1 2023 · DOI
- [20]Kwa T. West B. Becker J. Deng A. Garcia K. Hasin M. Measuring AI ability to complete long tasks [Internet] arXiv http://arxiv.org/abs/2503.14499 2025 · arXiv
- [23]Shah C. From prompt engineering to prompt science with Human in the loop [Internet] arXiv http://arxiv.org/abs/2401.04122 2024 · arXiv
- [24]Naveed H. Khan A.U. Qiu S. Saqib M. Anwar S. Usman M. A comprehensive overview of large language models [Internet] arXiv http://arxiv.org/abs/2307.06435 2024 · arXiv
- [25]Pantha N. Ramasubramanian M. Gurung I. Maskey M. Ramachandran R. Challenges in guardrailing large language models for science [Internet] arXiv http://arxiv.org/abs/2411.08181 2024 · arXiv
- [29]Bolton E. Venigalla A. Yasunaga M. Hall D. Xiong B. Lee T. BioMedLM: a 2.7B parameter language model trained on biomedical text [Internet] arXiv http://arxiv.org/abs/2403.18421 2024 · arXiv
- [30]Saab K. Tu T. Weng W.H. Tanno R. Stutz D. Wulczyn E. Capabilities of Gemini models in medicine [Internet] arXiv http://arxiv.org/abs/2404.18416 2024 · arXiv
- [32]Gao Y. Xiong Y. Gao X. Jia K. Pan J. Bi Y. Retrieval-augmented generation for large language models: a survey [Internet] arXiv http://arxiv.org/abs/2312.10997 2024 · arXiv
- [33]Lewis P. Perez E. Piktus A. Petroni F. Karpukhin V. Goyal N. Retrieval-augmented generation for knowledge-intensive NLP tasks Proceedings of the 34th International Conference on Neural Information Processing Systems 2020 Curran Associates Inc. Red Hook, NY, USA 9459 9474 NIPS ’20
- [34]Han H. Wang Y. Shomer H. Guo K. Ding J. Lei Y. Retrieval-augmented generation with graphs (GraphRAG) [Internet] arXiv http://arxiv.org/abs/2501.00309 2025 · arXiv
- [35]Nisa U. Shirazi M. Saip M.A. Pozi M.S.M. Agentic AI: the age of reasoning—A review J Autom Intell [Internet] 2025 Aug 28 [cited 2025 Oct 6]Available from https://www.sciencedirect.com/science/article/pii/S2949855425000516
- [41]McMahan B. Moore E. Ramage D. Hampson S. Arcas B.A. Communication-efficient learning of deep networks from decentralized data Proceedings of the 20th International Conference on Artificial Intelligence and Statistics [Internet]. PMLR 2017 1273 1282 [cited 2025 Oct 6]Available from https://proceedings.mlr.press/v54/mcmahan17a.html
- [44]Zhang F. Kreuter D. Chen Y. Dittmer S. Tull S. Shadbahr T. Recent methodological advances in federated learning for healthcare Patterns [Internet] 5 6 2024 June 14 [cited 2025 Oct 6]Available from https://www.cell.com/patterns/abstract/S2666-3899(24)00131-4 PMC11240178 39005485
- [45]Xu T. Feng J. Melendez J. Roberts K. Cai D. Zhu M. Addressing accuracy and hallucination of LLMs in Alzheimer’s disease research through knowledge graphs [Internet] arXiv http://arxiv.org/abs/2508.21238 2025 · arXiv
- [46]Skarlinski M.D. Cox S. Laurent J.M. Braza J.D. Hinks M. Hammerling M.J. Language agents achieve superhuman synthesis of scientific knowledge [Internet] arXiv http://arxiv.org/abs/2409.13740 2024 · arXiv
- [47]Pu K. Feng K.J.K. Grossman T. Hope T. Mishra B.D. Latzke M. IdeaSynth: iterative research idea development through evolving and composing idea facets with literature-grounded feedback Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems [Internet] 2025 1 31 [cited 2025 July 18]Available from http://arxiv.org/abs/2410.04025 · arXiv
- [48]Lu C. Lu C. Lange R.T. Foerster J. Clune J. Ha D. The AI scientist: towards fully automated open-ended scientific discovery [Internet] arXiv http://arxiv.org/abs/2408.06292 2024 · arXiv
- [49]Wei J. Yang Y. Zhang X. Chen Y. Zhuang X. Gao Z. From AI for science to agentic science: a survey on autonomous scientific discovery [Internet] arXiv http://arxiv.org/abs/2508.14111 2025 · arXiv
- [50]Gottweis J. Weng W.H. Daryin A. Tu T. Palepu A. Sirkovic P. Towards an AI co-scientist [Internet] arXiv http://arxiv.org/abs/2502.18864 2025 · arXiv
- [51]Ghareeb A.E. Chang B. Mitchener L. Yiu A. Szostkiewicz C.J. Laurent J.M. Robin: a multi-agent system for automating scientific discovery [Internet] arXiv http://arxiv.org/abs/2505.13400 2025 PMC13346116 42156546 · arXiv
- [52]Lahiri A.K. Hu Q.V. AlzheimerRAG: multimodal retrieval augmented generation for clinical use cases using PubMed articles [Internet] arXiv http://arxiv.org/abs/2412.16701 2025 · arXiv
- [53]Naumov V. Zagirova D. Lin S. Xie Y. Gou W. Urban A. DORA AI scientist: multi-agent virtual research team for scientific exploration discovery and automated report generation [Internet] bioRxiv https://www.biorxiv.org/content/10.1101/2025.03.06.641840v1 2025 · DOI
- [55]Swanson K. Wu W. Bulaong N.L. Pak J.E. Zou J. The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies Nature 2025 1 3 July 29 40730228
- [57]Istrate A.M. Milletari F. Castrotorres F. Tomczak J.M. Torkar M. Li D. rbio1-training scientific reasoning LLMs with biological world models as soft verifiers [Internet] bioRxiv https://www.biorxiv.org/content/10.1101/2025.08.18.670981v2 2025 · DOI
- [58]Chai J. Tang S. Ye R. Du Y. Zhu X. Zhou M. SciMaster: towards general-purpose scientific AI agents, part I X-Master Found: Can We Lead Humanity 19s Last Exam? [Internet] 2025 arXiv[cited 2025 Aug 29]Available from http://arxiv.org/abs/2507.05241 · arXiv
- [60]Roohani Y. Lee A. Huang Q. Vora J. Steinhart Z. Huang K. BioDiscoveryAgent: an AI agent for designing genetic perturbation experiments [Internet] arXiv http://arxiv.org/abs/2405.17631 2025 · arXiv
- [61]Ding N. Qu S. Xie L. Li Y. Liu Z. Zhang K. Automating exploratory proteomics research via language models [Internet] arXiv http://arxiv.org/abs/2411.03743 2024 · arXiv
- [62]Jin R. Zhang Z. Wang M. Cong L. STELLA: self-evolving LLM agent for biomedical research [Internet] arXiv http://arxiv.org/abs/2507.02004 2025 · arXiv
- [77]Saunders S. Gregory S. Clement M.H.S. Birck C. der Geyten S van Ritchie C.W The European Prevention of Alzheimer’s Dementia Programme: an Innovative Medicines Initiative-funded partnership to facilitate secondary prevention of Alzheimer’s disease dementia Front Neurol [Internet] 2022 13 Nov 22 [cited 2025 Aug 29]Available from https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1051543/full PMC9723139 36484017 · DOI
- [83]Cai Y. Zhou J. Scott P.W. Tian Q. Wanigatunga A.A. Lipsitz L. Physical activity complexity, cognition, and risk of cognitive impairment and dementia in the Baltimore Longitudinal Study of Aging Alzheimers Dement (N Y) 11 2 2025 e70077 PMC11982518 40225241
- [89]Nilforooshan R., Barnaghi P. The RESILIENT dataset: multimodal monitoring of ageing-related comorbidities and cognitive decline [Internet]. Zenodo; 2025 [cited 2025 Aug 29]. Available from: https://zenodo.org/records/16755408. PMC12546717 41125650
- [96]Lau O. Golder S. Comparison of elicit AI and traditional literature searching in evidence syntheses using four case studies Cochrane Evid Synth Methods 3 6 2025 e70050 Nov PMC12483133 41035533
- [97]Wang Z. Cao L. Jin Q. Chan J. Wan N. Afzali B. A foundation model for human-AI collaboration in medical literature mining [Internet] arXiv http://arxiv.org/abs/2501.16255 2025 PMC12460617 40993125 · arXiv
- [99]Qi B. Zhang K. Tian K. Li H. Chen Z.R. Zeng S. Large language models as biomedical hypothesis generators: a comprehensive evaluation [Internet] arXiv http://arxiv.org/abs/2407.08940 2024 · arXiv
- [101]Gabriel I. Manzini A. Keeling G. Hendricks L.A. Rieser V. Iqbal H. The ethics of advanced AI assistants [Internet] arXiv http://arxiv.org/abs/2404.16244 2024 · arXiv
- [102]Tang X. Jin Q. Zhu K. Yuan T. Zhang Y. Zhou W. Risks of AI scientists: prioritizing safeguarding over autonomy [Internet] arXiv http://arxiv.org/abs/2402.04247 2025 PMC12446425 40968279 · arXiv
Cited by
Sorted by citations
Reimagining biomedical science workflows in the age of large language models
Roberts, K. F., Koutarapu, S., Melendez, J., Landsness, E. C., Reese, J. T., Elbert, D. L., & Bateman, R. J. (2026) · npj Dementia · doi:10.1038/s44400-026-00126-3
Self-citation
- Authors
- Roberts, K. F., Landsness, E. C., Reese, J., Elbert, D., Strobel, G., Wu, E., Chen, Y., Lai, A., Abrams, Z. B., Zhu, M., Melendez, J., Koutarapu, S., Song, S., Chen, Y., Lazar, R., Barnaghi, P., Crary, J. F., Sardi, S. P., Voss, M. D., Krishnan, R., Schwartz, J. W., Mallon, R., Jimenez-Maggiora, G. A., Wang, C., Sandmann, T., Bose, N., Phatak, M., Wittenberg, G., Kevrekidis, Y. G., Mitchell, C. S., Mitchener, L., Raj, T., Foschini, L., Moore, G. J., & Bateman, R. J.
- Affiliations
- Washington University in St. Louis (US)
- Lawrence Berkeley National Laboratory (US)
- University of Washington (US)
- Alzforum (US)
- Booz Allen Hamilton (United States) (US)
- Imperial College London (GB)
- Icahn School of Medicine at Mount Sinai (US)
- Sanofi (United States) (US)
- Bristol-Myers Squibb (United States) (US)
- University of California, Santa Cruz (US)
- Denali Therapeutics (United States) (US)
- Sage Bionetworks (US)
- Johnson & Johnson (United States) (US)
- Johns Hopkins University (US)
- Georgia Institute of Technology (US)
- Future House, Inc. (US)
- Gates Ventures (US)
- Corresponding author
- Bateman, Randall J.
- ORCID
- Roberts, Kaleigh F.
- Landsness, Eric C.
- Reese, Justin
- Elbert, Donald
- Wu, Elizabeth
- Chen, Yixin
- Lai, Albert
- Abrams, Zachary B.
- Zhu, Mingfang
- Melendez, Justin
- Koutarapu, Srinivas
- Song, Sihui
- Chen, Yun
- Barnaghi, Payam
- Crary, John F.
- Sardi, Sergio Pablo
- Krishnan, Rajaraman
- Mallon, Ron
- Jimenez-Maggiora, Gustavo A.
- Wang, Chenguang
- Sandmann, Thomas
- Bose, Niranjan
- Phatak, Mukta
- Wittenberg, Gayle
- Kevrekidis, Yannis G.
- Mitchell, Cassie S.
- Mitchener, Ludovico
- Raj, Towfique
- Foschini, Luca
- Moore, Gregory J.
- Bateman, Randall J.
- Venue
- The Journal of Prevention of Alzheimer's Disease
- Publisher
- Elsevier BV
- Published
- 2026-01
- Volume
- 13
- Issue
- 1
- Pages
- 100398
- ISSN
- 2274-5807 (ISSN-L)
- PMC
- PMC12811770
- OpenAlex
- W4416832596
- Cited by
- 1
- References
- 69
- Citations by year
- 2026: 1
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Journal of Prevention of Alzheimer s Disease (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health (1r35gm152245-01)
- National Institute on Aging (P30AG066514, U19AG065169)
- National Institute of General Medical Sciences (R35 GM152245)
- National Institute of Neurological Disorders and Stroke (R01NS133365)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- neurodegenerative diseases, Alzheimer's disease and related dementias, literature synthesis, hypothesis generation, experimental design, data interpretation, foundation models, biomedical data integration, research acceleration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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