Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems
Open accessAbstract
There is a growing recognition of the need for clinical trials to safely and effectively deploy artificial intelligence (AI) in clinical settings. We introduce dynamic deployment as a framework for AI clinical trials tailored for the dynamic nature of large language models, making possible complex medical AI systems which continuously learn and adapt in situ from new data and interactions with users while enabling continuous real-time monitoring and clinical validation.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The existence of such frameworks does not ensure robust post-deployment oversight in routine care.
“However, the existence of such frameworks does not ensure robust post-deployment oversight in routine care”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [16]
Uptake of formal guidance in clinical AI trials remains incomplete.
“and uptake of formal guidance in clinical AI trials remains incomplete [15,16].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [16]
The References list identifies the CONSORT-AI and SPIRIT-AI guidance, and the Existing precedent section says those guidelines excluded continuously learning trial designs. Neither passage addresses how often clinical AI trials follow formal guidance (searched for guideline uptake, adoption, and compliance). (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works
Real-World Evidence — What Is It and What Can It Tell Us?
Sherman, R. E., Anderson, S. A., Pan, G. J. D., Gray, G. W., Gross, T. P., Hunter, N. L., LaVange, L. M., Marinac‐Dabic, D., Marks, P. W., Robb, M. A., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmsb1609216 · Cited by 2,134 · see citing works
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738 · see citing works
Continual Reassessment Method: A Practical Design for Phase 1 Clinical Trials in Cancer
O’Quigley, J., PEPE, M. S., & Fisher, L. D. (1990) · Biometrics · doi:10.2307/2531628 · Cited by 1,586 · see citing works
Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations
Chen, J. H., & Asch, S. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1702071 · Cited by 1,171 · see citing works
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Tabassi, E. (2023) · doi:10.6028/nist.ai.100-1 · Cited by 1,136 · see citing works
Deep reinforcement learning from human preferences
Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017) · arXiv · doi:10.48550/arxiv.1706.03741 · Cited by 1,058 · see citing works
Large Language Model Based Multi-agents: A Survey of Progress and Challenges
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., & Zhang, X. (2024) · Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/890 · Cited by 851 · see citing works
Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial
Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.40969 · Cited by 724 · see citing works
A Survey on In-context Learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30218-1 · Cited by 555 · see citing works
Do as AI say: susceptibility in deployment of clinical decision-aids
Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00385-9 · Cited by 543 · see citing works
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30219-3 · Cited by 486 · see citing works
Software development lifecycle models
Ruparelia, N. B. (2010) · SOEN · doi:10.1145/1764810.1764814 · Cited by 420 · see citing works
Han, R., Acosta, J. N., Shakeri, Z., Ioannidis, J. P. A., Topol, E. J., & Rajpurkar, P. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00047-5 · Cited by 336 · see citing works
Large Language Models Are Human-Level Prompt Engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022) · arXiv · doi:10.48550/arxiv.2211.01910 · Cited by 330 · see citing works
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236 · see citing works
An artificial intelligence life cycle: from conception to production
De Silva, D., & Alahakoon, D. (2022) · Patterns · doi:10.1016/j.patter.2022.100489 · Cited by 189 · see citing works
Artificial Intelligence in U.S. Health Care Delivery
Sahni, N. R., & Carrus, B. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2204673 · Cited by 175 · see citing works
Bridging the implementation gap of machine learning in healthcare
Seneviratne, M. G., Shah, N. H., & Chu, L. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000359 · Cited by 173 · see citing works
External validation of AI models in health should be replaced with recurring local validation
Youssef, A., Pencina, M., Thakur, A., Zhu, T., Clifton, D., & Shah, N. H. (2023) · Nature Medicine · doi:10.1038/s41591-023-02540-z · Cited by 170 · see citing works
Generative artificial intelligence in primary care: an online survey of UK general practitioners
Blease, C., Locher, C., Gaab, J., Hägglund, M., & Mandl, K. D. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101102 · Cited by 169 · see citing works
LLM-based agentic systems in medicine and healthcare
Qiu, J., Lam, K., Li, G., Acharya, A., Wong, T. Y., Darzi, A., Yuan, W., & Topol, E. J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00944-1 · Cited by 168 · see citing works
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.3386/w31422 · Cited by 161 · see citing works
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.2139/ssrn.4505053 · Cited by 161 · see citing works
AI-Generated Draft Replies Integrated Into Health Records and Physicians’ Electronic Communication
Tai-Seale, M., Baxter, S. L., Vaida, F., Walker, A., Sitapati, A. M., Osborne, C., Diaz, J., Desai, N., Webb, S., Polston, G., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.6565 · Cited by 158 · see citing works
Medical Artificial Intelligence and Human Values
Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K. (2024) · New England Journal of Medicine · doi:10.1056/nejmra2214183 · Cited by 149 · see citing works
Influence of believed AI involvement on the perception of digital medical advice
Reis, M., Reis, F., & Kunde, W. (2024) · Nature Medicine · doi:10.1038/s41591-024-03180-7 · Cited by 130 · see citing works
Characterizing the clinical adoption of medical AI devices through U.S. insurance claims
Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J. (2024) · NEJM AI · doi:10.1056/aioa2300030 · Cited by 103 · see citing works
Wan, P., Huang, Z., Tang, W., Nie, Y., Pei, D., Deng, S., Chen, J., Zhou, Y., Duan, H., Chen, Q., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03148-7 · Cited by 97 · see citing works
Optimizing generative AI by backpropagating language model feedback
Yuksekgonul, M., Bianchi, F., Boen, J., Liu, S., Lu, P., Huang, Z., Guestrin, C., & Zou, J. (2025) · Nature · doi:10.1038/s41586-025-08661-4 · Cited by 85 · see citing works
Not all AI health tools with regulatory authorization are clinically validated
Fassi, S. C. E., Abdullah, A., Fang, Y., Natarajan, S., Masroor, A. B., Kayali, N., Prakash, S., & Henderson, G. E. (2024) · Nature Medicine · doi:10.1038/s41591-024-03203-3 · Cited by 79 · see citing works
To do no harm — and the most good — with AI in health care
Goldberg, C. B., Adams, L. L., Blumenthal, D., Brennan, P. F., Brown, N., Butte, A. J., Cheatham, M., deBronkart, D., Dixon, J., Drazen, J. M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02853-7 · Cited by 72 · see citing works
To Do No Harm — and the Most Good — with AI in Health Care
Goldberg, C. B., Adams, L. L., Blumenthal, D., Brennan, P. F., Brown, N., Butte, A. J., Cheatham, M., deBronkart, D., Dixon, J., Drazen, J. M., et al. (2024) · NEJM AI · doi:10.1056/aip2400036 · Cited by 61 · see citing works
Why We Support and Encourage the Use of Large Language Models in NEJM AI Submissions
Koller, D., Beam, A. L., Manrai, A. K., Ashley, E. A., Liu, X., Gichoya, J. W., Holmes, C. C., Zou, J., Dagan, N., Wong, T. Y., et al. (2023) · NEJM AI · doi:10.1056/aie2300128 · Cited by 57 · see citing works
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare
Kim, J. Y., Boag, W., Gulamali, F., Hasan, A., Hogg, H. D. J., Lifson, M., Mulligan, D., Patel, M., Raji, I. D., Sehgal, A., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594089 · Cited by 55 · cited 1× · see citing works
“…fi cation, then proceeding to a design phase, followed by model development in the research setting, silent deployment, then deployment to “ production ” setting in the real-world, and fi nally a post-deployment phase of monitoring and making changes or removing from production as needed 8 – 11 .”
Rethinking Algorithm Performance Metrics for Artificial Intelligence in Diagnostic Medicine
Reyna, M. A., Nsoesie, E. O., & Clifford, G. D. (2022) · JAMA · doi:10.1001/jama.2022.10561 · Cited by 49 · see citing works
A strategy for cost-effective large language model use at health system-scale
Klang, E., Apakama, D. U., Abbott, E. E., Vaid, A., Lampert, J., Sakhuja, A., Freeman, R., Charney, A. W., Reich, D. L., Kraft, M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01315-1 · Cited by 49 · see citing works
Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings
Vaid, A., Sawant, A. S., Suárez‐Fariñas, M., Lee, J., Kaul, S. K., Kovatch, P., Freeman, R., Jiang, J. J., Jayaraman, P., Fayad, Z. A., et al. (2023) · Annals of Internal Medicine · doi:10.7326/m23-0949 · Cited by 46 · see citing works
Li, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104531 · Cited by 45 · see citing works
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning
Yue, L., Xing, S., Chen, J., & Fu, T. (2024) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3698587.3701359 · Cited by 42 · see citing works
Regulate Artificial Intelligence in Health Care by Prioritizing Patient Outcomes
Ayers, J. W., Desai, N., & Smith, D. M. (2024) · JAMA · doi:10.1001/jama.2024.0549 · Cited by 33 · see citing works
Abramoff, M. D., Dai, T., & Zou, J. (2024) · NEJM AI · doi:10.1056/aipc2400083 · Cited by 32 · see citing works
We need more randomized clinical trials of AI.
Ouyang, D., & Hogan, J. (2024) · NEJM AI · doi:10.1056/aie2400881 · Cited by 24 · see citing works
Neoadjuvant Exercise Therapy in Prostate Cancer
Jones, L. W., Moskowitz, C. S., Lee, C. P., Fickera, G. A., Chun, S. S., Michalski, M., Stoeckel, K., Underwood, W. P., Lavery, J. A., Bhanot, U. K., et al. (2024) · JAMA Oncology · doi:10.1001/jamaoncol.2024.2156 · Cited by 23 · see citing works
Implementing AI models in clinical workflows: a roadmap
Wang, F., & Beecy, A. (2024) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2023-112727 · Cited by 20 · see citing works
Self-citationA unifying force for the realization of medical AI
Lennerz, J. K., Green, U., Williamson, D. F. K., & Mahmood, F. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00721-7 · Cited by 19 · see citing works
Sendak, M., Liu, V. X., Beecy, A., Vidal, D. E., Shaw, K., Lifson, M. A., Tobey, D., Valladares, A., Loufek, B. T., Mogri, M., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae119 · Cited by 14 · see citing works
Self-citationSanchez, M., Alford, K., Krishna, V., Huynh, T. M., Nguyen, C. D. T., Lungren, M. P., Truong, S. Q. H., & Rajpurkar, P. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101207 · Cited by 13 · see citing works
An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare SystemsPreprint
Lu, C., Strout, J., Gauriau, R., Wright, B. A., Macruz, F., Buch, V., & Andriole, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07678 · Cited by 4 · see citing works
Direct Language Model Alignment from Online AI FeedbackPreprint
Guo, S., Zhang, B., Liu, T., Liu, T., Khalman, M., Llinares-López, F., Ramé, A., Mesnard, T., Zhao, Y., Piot, B., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.04792 · Cited by 4 · see citing works
Narrowing the gap: expected versus deployment performance
Zhou, A. X., Aczon, M., Laksana, E., Ledbetter, D., & Wetzel, R. C. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad100 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [4]Dhawan, N., Cotta, L., Ullrich, K., Krishnan, R. G. & Maddison, C. J. End-To-End Causal Effect Estimation from Unstructured Natural Language Data. In: Advances in Neural Information Processing Systems vol. 37, 77165–77199 (Curran Associates, Inc., 2024).
- [28]1. Rafailov, R. et al. Direct Preference Optimization: Your Language Model is Secretly a Reward Model. in Advances in Neural Information Processing Systems (eds. Oh, A. et al.) vol. 36, 53728–53741 (Curran Associates, Inc., 2023).
- [29]1. Dong, H. et al. RLHF Workflow: From Reward Modeling to Online RLHF. Transactions on Machine Learning Research (2024).
Cited by
Sorted by citations
Ogut, E. (2025) · Clinics and Practice · doi:10.3390/clinpract15090169 · Cited by 107 · see citing works
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 57 · see citing works
Assudani, P. J., Bhurgy, A. S., Kollem, S., Bhurgy, B. S., Ahmad, M. O., Kulkarni, M. B., & Bhaiyya, M. L. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.115802 · Cited by 35 · see citing works
Jain, S. S., Goto, S., Hall, J. L., Khan, S. S., MacRae, C. A., Ofori, C., Pegus, C., Pencina, M., Peterson, E. D., Schwamm, L. H., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001400 · Cited by 23 · see citing works
Zhao, J., Liu, H., Chen, Y., & Song, F. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03324-w · Cited by 25 · see citing works
Al-Ewaidat, O. A., & Naffaa, M. M. (2025) · Rheumato · doi:10.3390/rheumato5040017 · Cited by 24 · see citing works
Xu, X., & Sankar, R. (2025) · Information · doi:10.3390/info16100894 · Cited by 20 · see citing works
POC Sensor Systems and Artificial Intelligence—Where We Are Now and Where We Are Going?
Prashanthi, K., Kovur, K. M., Rayat, D. Y., & Wishart, D. S. (2025) · Biosensors · doi:10.3390/bios15090589 · Cited by 21 · see citing works
Artificial Intelligence for Cardiovascular Care in Action
Biswas, D., Thangaraj, P., Pedroso, A. F., Ogunniyi, M. O., Maddox, T. M., Ahmad, F. S., & Khera, R. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102307 · Cited by 15 · see citing works
The AI revolution: how multimodal intelligence will reshape the oncology ecosystem
Dellamonica, D., Ruau, D., Griffiths, B., Rossi, G., Li, B. T., Razavi, P., Maio, T. D., Gonzalez, T., Remorino, A., Reis-Filho, J. S., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00044-4 · Cited by 14 · see citing works
Al-Jebrni, A. H., Jiang, Z., Qin, Y., Qiu, Y., Jiang, N., Li, G., Zheng, Y., Wong, T. Y., Kim, S., Jung, Y., et al. (2026) · Computational Visual Media · doi:10.26599/cvm.2026.9450530 · Cited by 9 · see citing works
A clinical environment simulator for dynamic AI evaluation
Luo, L., Kim, S. E., Zhang, X., Kernbach, J. M., Kenia, R., Acosta, J. N., Nathanson, L. A., Haimovich, A. D., Rodman, A. M., Goh, E., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04252-6 · Cited by 11 · see citing works
Precision cardiovascular medicine with big data and AI
Xu, Q., Li, Y., Zhu, M., Cai, Y., Cheng, X., Wan, M., Ju, J., Xu, Y., Li, Y., & Liu, Y. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02538-0 · Cited by 8 · see citing works
SERS Meets Artificial Intelligence: A New Frontier in Cancer Diagnosis and Prognosis.
Huda, N. U., Bari, R. Z. A., Javed, M. A., Kiani, M. N., & Jin, Y.-D. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c00874 · Cited by 6 · see citing works
El Arab, R. A., Mustafa, M. H., Almagharbeh, W. T., Saleem, N. H., Al Abdulmohsen, S., Boathab, R., & Bu Washl, M. (2026) · Healthcare · doi:10.3390/healthcare14111459 · Cited by 4 · see citing works
“However, the existence of such frameworks does not ensure robust post-deployment oversight in routine care, and uptake of formal guidance in clinical AI trials remains incomplete [15,16].”
Chang, Q., Chen, F., Chen, Y., Cheng, L., Dong, D., Dong, J., Feng, X., Ge, J., He, J., He, Y., et al. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.09.001 · Cited by 4 · see citing works
Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma
Wu, Y., Zhang, D., Jiang, J., Zheng, L., Zhou, Z., Zhang, Z., & Nouri, S. (2025) · Clinical and Experimental Medicine · doi:10.1007/s10238-025-01987-3 · Cited by 5 · see citing works
Challenges in applying the EU AI act research exemptions to contemporary AI research
Mészáros, J., Huys, I., & Ioannidis, J. P. A. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02263-0 · Cited by 4 · see citing works
Savage, C. H., Sulam, J., Huang, C., & Yi, P. H. (2026) · American Journal of Roentgenology · doi:10.2214/ajr.26.34829 · Cited by 4 · see citing works
Garg, P., Singhal, G., & Singhal, S. S. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1750124 · Cited by 4 · see citing works
Farahani, S., Ieva, A. D., Coiera, E. W., & Liu, S. (2026) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2026.100092 · Cited by 3 · see citing works
The role of AI in oncology: present applications and future horizons
Weitzner, A. S., Singla, N., & Rai, A. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01408-y · Cited by 4 · see citing works
Structured reasoning failures compromise LLM interpretation of clinical oncology notes
Kenaston, M. W., Ayub, U., Parmar, M., Kumar, P., Anjum, M. U., Naqvi, S. A. A., Chaudhuri, A. A., Zakharia, Y., Heath, E. I., Bekaii-Saab, T. S., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02951-5 · Cited by 2 · see citing works
Jabeen, S., Bibi, M., Shah, S., Ali, A. A., & Bahloul, M. A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108816 · Cited by 2 · see citing works
Machine Learning in Clinical Trial Design and Optimization
Shaikh, K., Thanki, R. M., & Shaikh, A. (2026) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-10277-5_4 · Cited by 2 · see citing works
Basnet, R., Wu, Y., Liu, Z., & Li, Z. (2026) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/0113862073426310251010113112 · Cited by 2 · see citing works
Beyond the algorithm: why oncology nursing in Asia–Pacific needs evidence-based AI evaluation
Adapa, K., Kotte, S., Metta, M., Escobar-Agreda, S., Sethi, R., Patil, G., & Mazur, L. M. (2026) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2026.100939 · Cited by 2 · see citing works
Medical multimodal large language models: A survey
Xiao, H., Hui, N., Xu, Y., Li, Z., & Peng, J. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104386 · Cited by 2 · see citing works
AI for science: Progress, challenges, and perspectives
Xu, Y., Shao, Z., Liu, X., Li, Y., An, Z., Fu, C., Huang, C., Zhang, L., Gao, F., Li, J., et al. (2026) · The Innovation · doi:10.1016/j.xinn.2026.101530 · Cited by 3 · see citing works
Case Studies and Clinical Applications of AI in Imaging
Beohar, R., Purandare, D., Bhoite, S., Polshettiwar, D., & Polshettiwar, S. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5801-7.ch009 · Cited by 1 · see citing works
Artificial intelligence for surgical data management and decision support: lessons from wound care
Zavaleta‐Monestel, E., Arguedas‐Chacón, S., & Cordero-Bermudez, K. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1718436 · Cited by 1 · see citing works
Toward integrated sleep health: multimodal AI in Hang Hao Meng agent
Cai, M., Liang, S., Zhen, S., Wei, S., Sun, T., Liu, J., Mao, H., & Zhang, J. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02432-9 · Cited by 1 · see citing works
Taha, A. T. M., & Lukmanov, R. A. (2025) · Automatic Documentation and Mathematical Linguistics · doi:10.3103/s0005105525701419 · Cited by 1 · see citing works
Chow, J., & Patlas, M. N. (2026) · Japanese Journal of Radiology · doi:10.1007/s11604-026-02009-2 · Cited by 1 · see citing works
A Safety-Constrained Architecture for Adaptive Clinical Decision Support
Wendakoon, K., & Tripathi, N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28819-6_21 · Cited by 1 · see citing works
A roadmap for medical large language models: a review of foundations, applications, and challenges
Sha, Y.-Y., Yu, L., Lin, Z.-H., Kaur, A., Lou, Y., Gornale, S. S., Zhang, T., Wong, L. S., Wang, Z., Yan, Y., et al. (2026) · Military Medical Research · doi:10.1016/j.mmr.2026.100050 · Cited by 2 · see citing works
Niriella, M. A., Prabagar, K., Wijesingha, I. P., Jayathilleke, B., Goonasinghe, T. N., Lakshan, M. T. D., Silva, A. P. D., & Kithulwatta, I. (2026) · AI and Ethics · doi:10.1007/s43681-026-01248-3 · Cited by 1 · see citing works
AI-based augmentation of oncology clinical trials
Villa, A., Eadie, A. L., Synnott, D., Romanò, R., Piffoux, M., Wong, E. Y. T., Scheinerman, N., Tan, D. S. W., de Braud, F., Koopman, M., et al. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01189-0 · Cited by 3 · see citing works
Fosset, M., Pensier, J., Jung, B., Siddiqi, A., Mamdani, M. M., & Celi, L. A. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001621 · Cited by 1 · see citing works
Wang, S.-S., Gao, H., Lin, P., Qian, T.-C., Du, Y., & Xu, L. (2025) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v17.i10.109792 · Cited by 1 · see citing works
Downes, M., Erekat, A. N., & Kummer, B. R. (2025) · Seminars in Neurology · doi:10.1055/a-2769-6703
Williams, A., & Kovalerchuk, B. (2026) · Electronics · doi:10.3390/electronics15030580
Althammer, A., Hummel, A., Steghöfer, J.-P., Reichel, F., Kolonko, J., Hatfield, S., Fischer, M., Schlögl-Flierl, K., Ziethmann, P., Weiss, M., et al. (2026) · medRxiv · doi:10.64898/2026.02.04.26345355
Chiluveru, S., Hassan, R., Kayyala, H. S., & Eereli, R. (2026) · International Journal of Basic & Clinical Pharmacology · doi:10.18203/2319-2003.ijbcp20260446
Júnior, J. C. V. (2026) · Research Society and Development · doi:10.33448/rsd-v15i3.50733
Transformative Roles of Artificial Intelligence in Clinical Trials
Jie, C. H. E. N. (2026) · Statistics in Biosciences · doi:10.1007/s12561-026-09517-9
Rios-García, W., Broncano-Rivera, K., MariaFe-Martinez-Acuna, Via-y-Rada-Torres, A. D., Quintana-García, L. A., Marcilla, J. J. M., Mercado, C. A. S., & Rios-Garcia, A. A. (2026) · Pregnancy Hypertension · doi:10.1016/j.preghy.2026.101462
The tumor microenvironment: a dynamic ecosystem and therapeutic nexus in modern oncology
Zhang, M., Lu, Y., & Yuan, X. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1836055
Evaluating and Understanding Model Editing for Medical Vision Language Models
Zhu, G., Wu, C. E., & Shen, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37362-5_8
The fever without a thermometer: the global illusion of artificial intelligence monitoring
Greenhill, R. G. (2026) · IJQHC Communications · doi:10.1093/ijcoms/lyag036
Clinical Artificial Intelligence Systems in Dentistry: Designing for Safe Human-AI Collaboration
Hegde, S., & Vasa, R. (2026) · Artificial intelligence · doi:10.5772/intechopen.1015701
Toward Autonomous Clinics: Human–Robot Collaboration in Clinical Care
Wu, X., Zhang, J., Xu, M., Chu, H. K., Chen, X., Zhong, P., Liu, B., He, M., & Shi, D. (2026) · MedComm · doi:10.1002/mco2.70885 · Cited by 1 · see citing works
Yun-qiu, G., Liu, T., & Yang, Y. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1915941
Artificial intelligence in clinical trials: a transformative force
Sultana, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40572-3.00004-6
A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence
Ye, Z., Chen, Y., Huang, X., Chen, M., Wang, W., Zhu, H., Han, K., Wang, J., Lu, Q., Zhao, Y., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03155-7
Zhang, W.-Z., Feng, Z.-R., Liu, Y.-T., Zhong, R.-K., Chen, S.-Y., Li, Z.-X., Li, B.-Y., Liu, D.-N., Gao, L., & Hu, K.-W. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1915669
Post-Processing Matters: Enhancing Post-Hoc Explanation Methods in Medical Imaging
Repetto, S., Ciná, A. E., Anguita, D., Roli, F., & Oneto, L. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728537
Can autonomous Agentic-AI systems accelerate drug discovery and development in the next decade?
Chaturvedi, N., Haque, S., & Kumar, V. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag583
Artificial intelligence: Physicians and patients’ communication
Meo, S. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45973-3.00016-6
- Authors
- Rosenthal, J. T., Beecy, A., & Sabuncu, M. R.
- Affiliations
- Weill Cornell Medicine (US)
- Cornell University (US)
- Corresponding author
- Rosenthal, Jacob T.
- Venue
- npj Digital Medicine
- Published
- 2025-05-06
- Volume
- 8
- Issue
- 1
- Pages
- 252-252
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC12056174
- OpenAlex
- W4410111118
- Cited by
- 59
- References
- 54
- Citations by year
- 2026: 41 · 2025: 18
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (T32GM152349)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- clinical trials, artificial intelligence in healthcare, dynamic deployment, adaptive systems, large language models, continuous learning, real-time monitoring, clinical validation, AI safety
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
