Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center
Abstract
PURPOSE: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials. PATIENTS AND METHODS: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS: Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION: In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
BERT-based tools have been used for subject matching across various diseases while struggling with generalizability.
“Bidirectional Encoder Representations from Transformers (BERT)-based tools have been used for subject matching across various diseases while struggling with generalizability [4–7].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [5]
AI systems for clinical trial operations can parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening.
“Systems parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening [56,57,58,59,60,61,62].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [59]
Cites
Use of Ranks in One-Criterion Variance Analysis
Kruskal, W. H., & Wallis, W. A. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483441 · Cited by 13,912 · see citing works
What can natural language processing do for clinical decision support?
Demner‐Fushman, D., Chapman, W. W., & McDonald, C. J. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.08.007 · Cited by 690 · see citing works
The Role of Clinical Trial Participation in Cancer Research: Barriers, Evidence, and Strategies
Unger, J. M., Cook, E., Tai, E., & Bleyer, A. (2016) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_156686 · Cited by 665 · see citing works
Carlisle, B., Kimmelman, J., Ramsay, T., & MacKinnon, N. (2015) · Clinical Trials · doi:10.1177/1740774514558307 · Cited by 367 · see citing works
Adult cancer clinical trials that fail to complete: an epidemic?
Stensland, K. D., McBride, R. B., Latif, A., Wisnivesky, J., Hendricks, R., Roper, N., Boffetta, P., Hall, S. J., Oh, W. K., & Galsky, M. D. (2014) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju229 · Cited by 161 · see citing works
Ni, Y., Wright, J., Perentesis, J., Lingren, T., Deleger, L., Kaiser, M., Kohane, I., & Solti, I. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-015-0149-3 · Cited by 142 · see citing works
A National Cancer Clinical Trials System for the 21st Century
Board on Health Care Services (2010) · National Academies Press eBooks · doi:10.17226/12879 · Cited by 135 · see citing works
Ni, Y., Kennebeck, S., Dexheimer, J. W., McAneney, C. M., Tang, H., Lingren, T., Li, Q., Zhai, H., & Solti, I. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002887 · Cited by 115 · see citing works
Artificial Intelligence, Big Data, and Cancer
Kantarjian, H. M., & Yu, P. P. (2015) · JAMA Oncology · doi:10.1001/jamaoncol.2015.1203 · Cited by 105 · see citing works
Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing
Patel, N. M., Michelini, V. V., Snell, J. M., Balu, S., Hoyle, A. P., Parker, J. S., Hayward, M. C., Eberhard, D. A., Salazar, A. H., McNeillie, P., et al. (2017) · The Oncologist · doi:10.1634/theoncologist.2017-0170 · Cited by 103 · see citing works
Matching Patient Records to Clinical Trials Using Ontologies
Patel, C. O., Cimino, J. J., Dolby, J., Fokoue, A., Kalyanpur, A. A., Kershenbaum, A., Ma, L., Schonberg, E., & Srinivas, K. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76298-0_59 · Cited by 96 · see citing works
Automated matching software for clinical trials eligibility: Measuring efficiency and flexibility
Penberthy, L., Brown, R., Puma, F., & Dahman, B. A. (2010) · Contemporary Clinical Trials · doi:10.1016/j.cct.2010.03.005 · Cited by 84 · see citing works
A Deep Learning–Based Decision Support Tool for Precision Risk Assessment of Breast Cancer
He, T., Puppala, M., Ezeana, C. F., Huang, Y.-S., Chou, P.-H., Yu, X., Chen, S., Wang, L., Yin, Z., Danforth, R. L., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00121 · Cited by 51 · see citing works
Clauser, S. B., Johnson, M. R., O'Brien, D. M., Beveridge, J. M., Fennell, M. L., & Kaluzny, A. D. (2009) · Implementation Science · doi:10.1186/1748-5908-4-63 · Cited by 49 · see citing works
Chen, L., Grant, J., Cheung, W. Y., & Kennecke, H. F. (2013) · Journal of Oncology Practice · doi:10.1200/jop.2012.000763 · Cited by 18 · see citing works
Intelligence artificielle appliquée à la radiothérapie
Bibault, J., Burgun, A., & Giraud, P. (2017) · Cancer/Radiothérapie · doi:10.1016/j.canrad.2016.09.021 · Cited by 11 · see citing works
Beck, J. T., Vinegra, M., Dankwa‐Mullan, I., Torres, A., Simmons, C., Holtzen, H., Urman, A., Roper, N., Norden, A. D., Rammage, M., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.35.15_suppl.6501 · Cited by 9 · see citing works
New tools automatically match patients with clinical trials
Opar, A. (2013) · Nature Medicine · doi:10.1038/nm0713-793 · Cited by 8 · see citing works
Soysal, E., Lee, H.-J., Zhang, Y., Huang, L.-C., Chen, X., Wei, Q., Zheng, W. J., Chang, J. T., Cohen, T., Sun, J., et al. (2017) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12174 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
Asan, O., Bayrak, A. E., & Choudhury, A. (2020) · Journal of Medical Internet Research · doi:10.2196/15154 · Cited by 825 · see citing works
The Role of Artificial Intelligence in Early Cancer Diagnosis
Hunter, B., Hindocha, S., & Lee, R. W. (2022) · Cancers · doi:10.3390/cancers14061524 · Cited by 367 · see citing works
Artificial intelligence applied to clinical trials: opportunities and challenges
Askin, S., Burkhalter, D., Calado, G., & El Dakrouni, S. (2023) · Health and Technology · doi:10.1007/s12553-023-00738-2 · Cited by 285 · see citing works
Current AI technologies in cancer diagnostics and treatment
Tiwari, A., Mishra, S. N., & Kuo, T. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02369-9 · Cited by 245 · see citing works
Benefits and Risks of AI in Health Care: Narrative Review
Chustecki, M. (2024) · Interactive Journal of Medical Research · doi:10.2196/53616 · Cited by 208 · see citing works
“AI-powered programs can simulate and evaluate numerous potential treatments to predict their effectiveness against various diseases, optimizing the drug development process in clinical trials [34].”
The Lancet Breast Cancer Commission
Coles, C. E., Earl, H. M., Anderson, B. O., Barrios, C. H., Bienz, M., Bliss, J. M., Cameron, D., Cardoso, F., Cui, W., Francis, P. A., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)00747-5 · Cited by 198 · see citing works
The future of pharmaceuticals: Artificial intelligence in drug discovery and development
Fu, C., & Chen, Q. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101248 · Cited by 151 · see citing works
The emerging roles of artificial intelligence in cancer drug development and precision therapy
Liang, G., Fan, W., Luo, H., & Zhu, X. M. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.110255 · Cited by 149 · see citing works
Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective
Sufyan, M., Shokat, Z., & Ashfaq, U. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107356 · Cited by 149 · see citing works
An Overview of Artificial Intelligence in Oncology
Farina, E. M. J. D. M., Nabhen, J. J., Dacoregio, M. I., Batalini, F., & de Moraes, F. Y. (2022) · Future Science OA · doi:10.2144/fsoa-2021-0074 · Cited by 137 · see citing works
Haddad, T. C., Helgeson, J. M., Pomerleau, K., Preininger, A. M., Roebuck, M. C., Dankwa‐Mullan, I., Jackson, G. P., & Goetz, M. P. (2021) · JMIR Medical Informatics · doi:10.2196/27767 · Cited by 79 · see citing works
Harnessing artificial intelligence to improve clinical trial design
Zhang, B., Zhang, L., Chen, Q., Jin, Z., Liu, S., & Zhang, S. (2023) · Communications Medicine · doi:10.1038/s43856-023-00425-3 · Cited by 80 · see citing works
Lu, X., Yang, C., Liang, L., Hu, G., Zhong, Z., & Jiang, Z. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae243 · Cited by 77 · see citing works
“The output produced from WCTM’s trial intake process details criteria for the trial and provides additional information related to clinical concepts and values with associated WCTM attributes 22 ; (3) Enhancing patient identification.”
BackgroundMethodHallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 80 · see citing works
Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer
Bitterman, D. S., Miller, T. A., Mak, R. H., & Savova, G. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.01.044 · Cited by 68 · see citing works
Chow, R., Midroni, J., Kaur, J., Boldt, G., Liu, G., Eng, L., Liu, F.-F., Haibe-Kains, B., Lock, M., & Raman, S. (2023) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djad013 · Cited by 65 · see citing works
Open challenges and opportunities in federated foundation models towards biomedical healthcare
Li, X., Peng, L., Wang, Y., & Zhang, W. (2025) · BioData Mining · doi:10.1186/s13040-024-00414-9 · Cited by 71 · see citing works
Artificial intelligence-based biomarkers for treatment decisions in oncology
Ligero, M., Nahhas, O. S. M. E., Aldea, M. D., & Kather, J. N. (2025) · Trends in cancer · doi:10.1016/j.trecan.2024.12.001 · Cited by 70 · see citing works
Perceptions and detection of AI use in manuscript preparation for academic journals
Chemaya, N., & Martin, D. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304807 · Cited by 67 · see citing works
Idnay, B., Dreisbach, C., Weng, C., & Schnall, R. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab228 · Cited by 54 · see citing works
Scoping review of the current landscape of AI-based applications in clinical trials
Cascini, F., Beccia, F., Causio, F. A., Melnyk, A., Zaino, A., & Ricciardi, W. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.949377 · Cited by 45 · see citing works
The role of artificial intelligence in hastening time to recruitment in clinical trials
Ismail, A., Al-Zoubi, T., Naqa, I. E., & Saeed, H. (2023) · BJR|Open · doi:10.1259/bjro.20220023 · Cited by 47 · see citing works
AI in Healthcare: medical and socio-economic benefits and challengesPreprint
Shaheen, M. Y. (2021) · doi:10.14293/s2199-1006.1.sor-.pprqni1.v1 · Cited by 43 · see citing works
Liu, T., Gao, Z., & Guan, H. (2021) · Complexity · doi:10.1155/2021/5588650 · Cited by 42 · see citing works
RetractedNext-Generation Implementation of Chimeric Antigen Receptor T-Cell Therapy Using Digital Health
Banerjee, R. K., Shah, N. D., & Dicker, A. P. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00023 · Cited by 38 · see citing works
Application of Information Technology to Clinical Trial Evaluation and Enrollment: A Review
von Itzstein, M. S., Hullings, M., Mayo, H., Beg, M. S., Williams, E. L., & Gerber, D. E. (2021) · JAMA Oncology · doi:10.1001/jamaoncol.2021.1165 · Cited by 36 · see citing works
Artificial intelligence in oncology: From bench to clinic
Elkhader, J. A., & Elemento, O. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.04.013 · Cited by 39 · see citing works
Bueschbell, B., Caniceiro, A. B., Suzano, P. M. S., Machuqueiro, M., Rosário‐Ferreira, N., & Moreira, I. S. (2022) · Drug Resistance Updates · doi:10.1016/j.drup.2022.100811 · Cited by 37 · see citing works
Accelerating drug discovery, development, and clinical trials by artificial intelligence
Zhang, Y., Mastouri, M., & Zhang, Y. (2024) · Med · doi:10.1016/j.medj.2024.07.026 · Cited by 32 · see citing works
Evolution of artificial intelligence as a modern technology in advanced cancer therapy
Khan, M. S., Alshahrani, M. Y., Wahab, S., Gupta, G., & Kesharwani, P. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2024.105892 · Cited by 35 · see citing works
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting (Print) · doi:10.1080/1061186x.2024.2448711 · Cited by 18 · see citing works
Meystre, S. M., Heider, P. M., Cates, A., Bastian, G., Pittman, T., Gentilin, S. M., & Kelechi, T. J. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01916-6 · Cited by 30 · see citing works
Kirshner, J. J., Cohn, K., Dunder, S., Donahue, K., Richey, M., Larson, P., Sutton, L. H., Siu, E., Donegan, J., Chen, Z., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00180 · Cited by 21 · see citing works
A comprehensive review of artificial intelligence for pharmacology research
Li, B., Tan, K., Lao, A. R., Wang, H., Zheng, H., & Zhang, L. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1450529 · Cited by 24 · see citing works
Artificial intelligence, medications, pharmacogenomics, and ethics
Haga, S. B. (2024) · Pharmacogenomics · doi:10.1080/14622416.2024.2428587 · Cited by 24 · see citing works
Applying advanced technologies to improve clinical trials: a systematic mapping study
Ngayua, E. N., He, J., & Agyei-Boahene, K. (2020) · Scientometrics · doi:10.1007/s11192-020-03774-1 · Cited by 19 · see citing works
Reimagining Global Oncology Clinical Trials for the Postpandemic Era: A Call to Arms
Saini, K. S., Heras, B. D. L., Plummer, R., Moreno, V., Romano, M., de Castro, J., Aftimos, P. G., Fredriksson, J., Bhattacharyya, G. S., Olivo, M. S., et al. (2020) · JCO Global Oncology · doi:10.1200/go.20.00346 · Cited by 19 · see citing works
Bikou, A., Deligianni, E., Dermiki‐Gkana, F., Liappas, N., Teriús-Padrón, J. G., Beltrán, M. E., Cabrera-Umpiérrez, M. F., & Kontogiorgis, C. A. (2024) · Journal of Medical Internet Research · doi:10.2196/60504 · Cited by 20 · see citing works
Challenges and applications in generative AI for clinical tabular data in physiology
Umesh, C., Mahendra, M., Bej, S., Wolkenhauer, O., & Wolfien, M. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03024-w · Cited by 19 · see citing works
Potential application of artificial intelligence in cancer therapy
Riaz, I. B., Khan, M. A., & Haddad, T. C. (2024) · Current Opinion in Oncology · doi:10.1097/cco.0000000000001068 · Cited by 19 · see citing works
Beyond boundaries: exploring the transformative power of AI in pharmaceuticals
Suri, G. S., Kaur, G., & Shinde, D. A. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00192-7 · Cited by 19 · see citing works
Wang, K., Cui, H., Zhu, Y., Hu, X., Chang, H. H., Guo, Y., An, L., Zhang, Q., & Liu, L. (2024) · BMC Cancer · doi:10.1186/s12885-024-11959-7 · Cited by 17 · see citing works
Saady, M., Eissa, M., Yacoub, A. S., Hamed, A. B., & Azzazy, H. M. E.-S. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103066 · Cited by 15 · see citing works
AI in Healthcare: medical and socio-economic benefits and challengesPreprint
Shaheen, M. Y. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3932277 · Cited by 15 · see citing works
Use of AI as a Research Recruitment Tool: Ethical Elements, Considerations, and Recommendations
Rentzepis, P. J., Snyder, D. L., Baker, T. J., & Bierer, B. E. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2649234 · Cited by 14 · see citing works
Broadening the horizon: potential applications of CAR-T cells beyond current indications
Karsten, H., Matrisch, L., Cichutek, S., Fiedler, W., Alsdorf, W. H., & Block, A. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1285406 · Cited by 15 · see citing works
AI in Healthcare: medical and socio-economic benefits and challengesPreprint
Shaheen, M. Y. (2021) · doi:10.31219/osf.io/um38t · Cited by 14 · see citing works
Alqahtani, A. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/6201067 · Cited by 13 · see citing works
RetractedGhosh, S., Abushukair, H. M., Ganesan, A., Pan, C., Naqash, A. R., & Lü, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0311510 · Cited by 17 · see citing works
Wu, J., Yakubov, A., Abdul‐Hay, M., Love, E., Kroening, G., Cohen, D., Spalink, C., Joshi, A., Balar, A. V., Joseph, K.-A., et al. (2021) · JCO Oncology Practice · doi:10.1200/op.21.00629 · Cited by 10 · see citing works
Molecular-based precision oncology clinical decision making augmented by artificial intelligence
Zeng, J., & Shufean, M. A. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210220 · Cited by 11 · see citing works
Casolino, R., Mateo, J., de Vries, E. G. E., Johns, A. J., Courtot, M., Lawlor, R. T., Perdomo, S., Gray, S. W., Pérez-López, R., Turnbull, C., et al. (2026) · The Lancet Oncology · doi:10.1016/s1470-2045(26)00302-5 · Cited by 10 · see citing works
Is There a Role of Artificial Intelligence in Preclinical Imaging?
Küper, A. T., Blanc‐Durand, P., Gafita, A., Kersting, D., Fendler, W. P., Seibold, C., Moraitis, A., Lückerath, K., James, M. L., & Seifert, R. (2023) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2023.03.003 · Cited by 10 · see citing works
Clinical Informatics Approaches to Understand and Address Cancer Disparities
Chaunzwa, T. L., del Rey, M. Q., & Bitterman, D. S. (2022) · Yearbook of Medical Informatics · doi:10.1055/s-0042-1742511 · Cited by 10 · see citing works
Rani, S., & Jain, A. (2023) · Multimedia tools and applications · doi:10.1007/s11042-023-15539-y · Cited by 7 · see citing works
Hurd, T. C., Payton, F. C., & Hood, D. B. (2024) · Milbank Quarterly · doi:10.1111/1468-0009.12712 · Cited by 9 · see citing works
Lee, K., Mai, Y., Liu, Z., Raja, K., Higashi, M. K., Jun, T., Ma, M., Wang, T., Ai, L., Calay, E., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77447-x · Cited by 13 · see citing works
Fernández, M. A., Astobiza, A. M., & Lozano, R. O. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11306-7 · Cited by 10 · see citing works
Review of the Performance Metrics for Natural Language Systems for Clinical Trials Matching
Kim, J., & Quintana, Y. (2022) · Studies in health technology and informatics · doi:10.3233/shti220156 · Cited by 8 · see citing works
Gui, X., Lv, H., Wang, X., Lv, L., Xiao, Y., & Wang, L. (2025) · BioData Mining · doi:10.1186/s13040-025-00458-5 · Cited by 7 · see citing works
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
Mallahan, S., Gajra, A., Blau, S. H., Peguero, J. A., Shoukier, M., Kanaan, M. N., Huynh, C. T., Efiom-Ekaha, D. N., Vankayala, H. M., Kitchen, K., et al. (2024) · AI in Precision Oncology · doi:10.1089/aipo.2024.0030 · Cited by 5 · see citing works
Vaterkowski, M., Daniel, C., Rosa, A. L., Kalra, D., Jaulent, M., Lamé, G., & Kempf, E. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105900 · Cited by 7 · see citing works
Rosa, A. L., Vaterkowski, M., Marc Cuggia, A., Campillo‐Gimenez, B., Tournigand, C., BAUJAT, B., Daniel, C., Kempf, E., & Lamé, G. (2024) · Cancer Medicine · doi:10.1002/cam4.70466 · Cited by 4 · see citing works
A unified framework for pre-screening and screening tools in oncology clinical trials
Horgan, D., Paulson, J., Loaiza‐Bonilla, A., Svedman, C., Malapelle, U., Penault-Llorca, F. M., Rasheed, H. M. A., Hofman, P. M., Kachnowski, S., Schneider, D. J., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01306-3 · Cited by 5 · see citing works
AI-enhanced patient-centric clinical trial design
Gupta, Y., Srivastava, V. P., & Singh, R. K. (2025) · AIP conference proceedings · doi:10.1063/5.0247858 · Cited by 5 · see citing works
Revolutionizing Health Records: The AI Way
Saranya, S. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24417190214 · Cited by 5 · see citing works
Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare
Sahoo, P., Kundu, M., & Begum, J. (2024) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010298852240528123911 · Cited by 8 · see citing works
The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects
de Oliveira, M. B. M., Mendes, F., Martins, M., Cardoso, P., Fonseca, J., Mascarenhas, T., & Mascarenhas, M. (2025) · Diagnostics · doi:10.3390/diagnostics15030274 · Cited by 5 · see citing works
Versteijne, E., Suker, M., Groen, J. V., Besselink, M. G., Bonsing, B. A., Bosscha, K. P., Busch, O. R., de Hingh, I. H. J. T., de Jong, K. P., Molenaar, I. Q., et al. (2020) · Annals of Surgery · doi:10.1097/sla.0000000000004364 · Cited by 5 · see citing works
RE: Use of artificial intelligence for cancer clinical trial enrollment
Deng, J., & Heybati, K. (2023) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djad228 · Cited by 5 · see citing works
Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review
Knapen, D. G., van Kruchten, M., de Groot, D. J. A., Broekman, K. E., & Fehrmann, R. S. N. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100682 · Cited by 6 · see citing works
Dafni, M. F., Shih, M., Manoel, A. Z., Yousif, M., Spathi, S., Harshal, C., Bhatt, G., Chodnekar, S. Y., Stam, N., Rasool, W., et al. (2024) · Cancer Causes & Control · doi:10.1007/s10552-024-01942-9 · Cited by 4 · see citing works
Artificial Intelligence in Oncology
Jeziorski, K., & Olszewski, R. (2024) · Applied Sciences · doi:10.3390/app15010269 · Cited by 4 · see citing works
Mehari, M., Warrier, G., Dada, A., Kabir, A., Haskell-Mendoza, A. P., Tripathy, A., Jha, R., Nieblas‐Bedolla, E., Jackson, J. D., Gonzalez, A. T., et al. (2025) · Science Advances · doi:10.1126/sciadv.adt5708 · Cited by 5 · see citing works
Mbatha, S. Z., Mulaudzi, T. V., Mbita, Z., Adeola, H. A., Batra, J., Blenman, K., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-36461-7_7 · Cited by 5 · see citing works
Guillotel, L.-A., Lesimple, T., Zékri, O., Marc Cuggia, A., & Campillo‐Gimenez, B. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106301 · Cited by 3 · see citing works
Research and application of omics and artificial intelligence in cancer
Zhang, Y., Ma, W., Huang, Z., Liu, K., Feng, Z., Zhang, L., Li, D., Mo, T., & Liu, Q. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad6951 · Cited by 3 · see citing works
Chadha, S., Mukherjee, S., & Sanyal, S. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152349 · Cited by 3 · see citing works
Molefi, T., Marima, R. M., Demetriou, D., Basera, A., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_13 · Cited by 4 · see citing works
Challenges, Limitations, and Ethical Considerations of AI in Immunology and Healthcare
Rajchenberg, D., Hess, R., Lins, M. P., Manor, A., Degany, O., & Shoenfeld, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch014 · Cited by 3 · see citing works
Ronquillo, J. G., & Lester, W. T. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00018 · Cited by 3 · see citing works
Goodwin, A. J., Armstrong, S. A., Ptak, D., Catchpole, K. C., Obeid, J. S., & Heider, P. M. (2025) · JMIR Formative Research · doi:10.2196/71831 · Cited by 2 · see citing works
Multi-objective optimization formulation for Alzheimer’s disease trial patient selection
Moayedikia, A., Fin, S., & Wiil, U. K. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104955 · Cited by 2 · see citing works
Gedor, M., Désandes, E., Chesnel, M., Merlin, J., Marchal, F., Lambert, A., & Baudin, A. (2024) · Bulletin du Cancer · doi:10.1016/j.bulcan.2024.01.010 · Cited by 2 · see citing works
Mkhize‐Kwitshana, Z. L., Naidoo, P., Duma, Z., Saini, K. S., & Dlamini, Z. (2024) · Springer eBooks · doi:10.1007/978-3-031-52860-6_10 · Cited by 3 · see citing works
Clinical Data Abstraction: A Research Study.
Watzlaf, V. J. M., Sheridan, P. T., Alzu'bi, A. A., & Chau, L. (2021) · PubMed Central · Cited by 2 · see citing works
Artificial intelligence in the treatment of cancer: Changing patterns, constraints, and prospects
Ali, M., Wani, S. U. D., Dey, T., & Mehdi, S. (2024) · Health and Technology · doi:10.1007/s12553-024-00825-y · Cited by 2 · see citing works
Benbow, J. H., & Kim, E. S. (2025) · The Cancer Journal · doi:10.1097/ppo.0000000000000796 · Cited by 1 · see citing works
Pollák, P., Barótfi, S., Csóka, I., & Budai-Szűcs, M. (2025) · Orvosi Hetilap · doi:10.1556/650.2025.33420 · Cited by 1 · see citing works
Claessens, A., Simon, A., Manchart, A., Desmonts, L., Leguay, C., Faivre, J.-C., Cambier, S., Salleron, J., & Lambert, A. (2026) · JMIR Cancer · doi:10.2196/80268 · Cited by 2 · see citing works
Korsah, M. A., Sewu, D. D., Patel, P., Jeevanandam, J., Ponnuchamy, K., & Danquah, M. K. (2026) · Next research. · doi:10.1016/j.nexres.2026.101735 · Cited by 1 · see citing works
A review on artificial intelligence in pharmaceutical sciences: opportunities and challenges
Kumar, V., Kumar, U., Kumar, R., Tiwari, N., & Gupta, J. K. (2026) · Drug Development and Industrial Pharmacy · doi:10.1080/03639045.2026.2670435 · Cited by 1 · see citing works
Kayira, A. B., Elyazori, H. R. A., Lybarger, K., Walter, F. M., Chelala, C., & Funston, G. (2026) · JMIR AI · doi:10.2196/73481 · Cited by 1 · see citing works
Das, S. K., Talukdar, A. D., Nath, D., & Choudhury, M. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15280-1.00007-8 · Cited by 1 · see citing works
Lutz, J., Pratap, A., Lenze, E. J., Bestha, D. P., Lipschitz, J. M., Karantzoulis, S., Vaidyanathan, U., Robin, J., Horan, W. P., Brannan, S. K., et al. (2023) · PubMed Central · Cited by 1 · see citing works
HARNESSING ARTIFICIAL INTELLIGENCE FOR REAL-TIME QUALITY ASSURANCE IN MEDICAL DEVICE MANUFACTURING
Barla, P. C., & Karthikeyan, L. (2024) · The American Journal of Engineering And Technology · doi:10.37547/tajet/volume06issue06-04 · Cited by 2 · see citing works
Naidu, G., & Krishnan, V. (2025) · International Journal of Intelligence Science · doi:10.4236/ijis.2025.151002 · Cited by 3 · see citing works
Lee, K., Mai, Y., Liu, Z., Raja, K., Jun, T. W., Ma, M., Wang, T., Ai, L., Calay, E. S., Oh, W. K., et al. (2024) · medRxiv · doi:10.1101/2024.02.28.24303396 · Cited by 1 · see citing works
ACCC Can Close the Gap in Cancer Research: The Research Program at Highlands Oncology Group
Patton, A. (2021) · Oncology Issues · doi:10.1080/10463356.2020.1849905
- Authors
- Beck, J. T., Rammage, M., Jackson, G. P., Preininger, A. M., Dankwa-Mullan, I., Roebuck, M. C., Torres, A., Holtzen, H., Coverdill, S. E., Williamson, M. P., Chau, Q., Rhee, K., & Vinegra, M.
- Affiliations
- Highlands Oncology Group (US)
- IBM (United States) (US)
- IBM Watson Health, IBM Corporation, Cambridge, MA
- RxEconomics, Hunt Valley, MD
- Novartis (United States) (US)
- ORCID
- Venue
- JCO Clinical Cancer Informatics
- Published
- 2020-11
- Volume
- 4
- Issue
- 4
- Pages
- 50-59
- ISSN
- 2473-4276 (ISSN-L)
- OpenAlex
- W3001688612
- Cited by
- 127
- References
- 19
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 33 · 2023: 20 · 2022: 7 · 2021: 16 · 2020: 5
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JCO Clinical Cancer Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- clinical trial eligibility screening, natural language processing, machine learning, breast cancer, cancer clinical trials, trial accrual, attribute extraction
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Artificial Intelligence, Breast Neoplasms, Clinical Trials as Topic, Eligibility Determination, Female, Humans, Middle Aged, Natural Language Processing, Patient Selection, Community Networks, Early Detection of Cancer
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 ›
Are you an author, or do you see a problem with this page? Request a review
