The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Medical Artificial Intelligence and Human Values
Open accessAbstract
Recent progress in generative artificial intelligence (AI) has given rise to large language models (LLMs) that can be prompted to craft persuasive essays, 1 pass professional competency exams, [2][3][4] and write patient-friendly empathetic messages.5 Amid growing recognition of the capabilities of LLMs, many have expressed concerns about their use in medicine and healthcare, citing known risks of confabulation, fragility, and factual inaccuracy.6 As these risks are measured and mitigated, what is coming into focus are unresolved questions about the human values that will remain embedded in AI, both in their creation and in their use, and how the "values of an LLM" may misalign with human values even if AI models no longer confabulate and have been scrubbed of obvious toxic output.Such "human values" pertain broadly to the principles, standards, and preferences that reflect human goals and guide human behaviors (Glossary).As we review here, LLMs and new foundation models, as technically impressive as they are, are only the latest incarnation of a long line of probabilistic models integrated into medical decision-making, all of which have required their creators and implementers to make value judgments.Many of the challenges we address here were evident to the pioneers of medical decision analysis of the 1950s 7 and to scholars in subsequent decades [8][9][10][11] who conducted careful and creative studies of both human and algorithmic decision-making to disentangle probability, the chance of an event occurring, from utilities, the quantified value judgments that are often only indirectly articulated in much of medical decision-making (Glossary).The nuanced understanding of individual values and risks is what makes the thoughtful clinician so indispensable.These considerations have renewed relevance with unprecedented and ubiquitously available AI models like LLMs.In this article, we first describe how value judgments enter predictive models, using familiar clinical settings and new AI language models.We then connect early work in reasoning about probabilities and utilities to the emerging issues of newer AI models, identifying unresolved challenges and future opportunities in designing high-performance and safe AI models.
Overview
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
Verification
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [10]
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.
Citations
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., Filiberti, A., Flechtner, H., Fleishman, S. B., Haes, J. C. J. M. D., et al. (1993) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/85.5.365 · Cited by 16,005
Levey, A. S., Bosch, J. P., Lewis, J. B., Greene, T., Rogers, N., Roth, D., & for the Modification of Diet in Renal Disease Study Group* (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00002 · Cited by 15,079
KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease
Stevens, P. E., Ahmed, S. B., Carrero, J. J., Foster, B., Francis, A., Hall, R. K., Herrington, W. G., Hill, G., Inker, L. A., Kazancıoğlu, R., et al. (2024) · Kidney International · doi:10.1016/j.kint.2023.10.018 · Cited by 8,530
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
EQ-SD: a measure of health status from the EuroQol Group
Rabin, R., & Charro, F. D. (2001) · Annals of Medicine · doi:10.3109/07853890109002087 · Cited by 5,613
New creatinine- and cystatin C-based equations to estimate GFR without race
Inker, L. A., Eneanya, N. D., Coresh, J., Tighiouart, H., Wang, D., Sang, Y., Crews, D. C., Doria, A., Estrella, M. M., Froissart, M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2102953 · Cited by 4,695
Research works printed but not linked: 2
- Stats STAT! NEJM Evidence (https://evidence.nejm.org/browse/evidence-media-type/stats-stat).
- Mate A Madaan L Taneja A et al. Field study in deploying restless multi-armed bandits: assisting non-profits in improving maternal and child health. In: Proceedings and Abstracts of the 36th AAAI Conference on Artificial Intelligence February 22–March 1 2022. Virtual: Association for the Advancement of Artificial Intelligence 2022 (https://cdn.aaai.org/ojs/21460/21460-13-25473-1-2-20220628.pdf).
Cited by
Sorted by citations
Advances in molecular pathology and therapy of non-small cell lung cancer
Huang, Q., Li, Y., Huang, Y., Wu, J., Bao, W., Xue, C., Li, X., Dong, S., Dong, Z., & Hu, S. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02243-6 · Cited by 197
El Arab, R. A., Abu-Mahfouz, M. S., Abuadas, F. H., Alzghoul, H., Almari, M., Ghannam, A., & Seweid, M. M. (2025) · Healthcare · doi:10.3390/healthcare13070701 · Cited by 108
“Despite regulatory frameworks such as GDPR, gaps persist in ensuring ethical data usage, especially in cross-border applications [29,31].”
Triola, M. M., & Rodman, A. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005963 · Cited by 92
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.
Cecconi, M., Greco, M., Shickel, B., Angus, D. C., Bailey, H., Bignami, E., Calandra, T., Celi, L. A., Einav, S., Elbers, P., et al. (2025) · Critical Care · doi:10.1186/s13054-025-05532-2 · Cited by 80
“Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11].”
Demographic bias of expert-level vision-language foundation models in medical imaging
Yang, Y., Liu, Y., Liu, X., Gulhane, A., Mastrodicasa, D., Wu, W., Wang, E. J., Sahani, D. V., & Patel, S. (2025) · Science Advances · doi:10.1126/sciadv.adq0305 · Cited by 67
Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems
Rosenthal, J. T., Beecy, A., & Sabuncu, M. R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01674-3 · Cited by 59
Heinrichs, H., Kies, A., Nagel, S. K., & Kiessling, F. (2025) · Journal of Medical Internet Research · doi:10.2196/74187 · Cited by 66
“Health care workers must adapt to these challenges amid persistent workforce shortages [1,2], with Europe alone facing an expected shortfall of 4 million health care workers by 2030 [3].”
Artificial Intelligence In Health And Health Care: Priorities For Action
Matheny, M. E., Goldsack, J. C., Saria, S., Shah, N. H., Gerhart, J. L., Cohen, I. G., Price, W. N., Patel, B., Payne, P., Embí, P. J., et al. (2025) · Health Affairs · doi:10.1377/hlthaff.2024.01003 · Cited by 74
“30 Last, there is a need for increased investments in research regarding health care operations, including how to employ AI to optimize workflows, resource allocation, scheduling, supply chain, and revenue-cycle operations, among others.”
A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41
Nong, P., & Ji, M. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf031 · Cited by 40
The Ethics of AI in Healthcare: An Updated Mapping Review
Morley, J., & Floridi, L. (2025) · The International Library of Bioethics · doi:10.1007/978-3-031-94690-5_2 · Cited by 42
Economics and Equity of Large Language Models: Health Care Perspective
Nagarajan, R., Kondo, M., Salas, F., Sezgın, E., Yao, Y., Klotzman, V., Godambe, S. A., Khan, N., Limon, A., Stephenson, G. S., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/64226 · Cited by 39
“Such a model is also expected to be better utilized while demonstrating value, since the training data can significantly impact its behavior and performance [115] .”
Odone, A., Barbati, C., Amadasi, S., Schultz, T., & Resnik, D. B. (2025) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(25)00412-8 · Cited by 39
Shin, Y., Lee, M., Lee, Y. K., Kim, K., & Kim, T. J. (2025) · Life · doi:10.3390/life15040654 · Cited by 40
“…reference datasets by experts to standardize algorithm evaluation, validating AI and machine learning tools using multi-institutional data to ensure generalizability, and implementing periodic model retraining and regular output monitoring to mitigate risks and align AI with human values [6,130].”
Mehta, N., Mehta, S., Rubenstein, A., & Wood, S. K. (2025) · Perspectives on Medical Education · doi:10.5334/pme.2233 · Cited by 32
Large language models for clinical decision support in gastroenterology and hepatology
Wiest, I. C., Bhat, M., Clusmann, J., Schneider, C. V., Jiang, X., & Kather, J. N. (2025) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-025-01108-1 · Cited by 34
“This aspect necessitates specific guardrails to ensure fairness and reproducibility 182 .”
Sousa‐Pinto, B., Marques‐Cruz, M., Neumann, I., Chi, Y., Nowak, A. J., Reinap, M., Awad, M., Nothacker, M., Trucl, M., Brożek, J., et al. (2025) · Annals of Internal Medicine · doi:10.7326/annals-24-02338 · Cited by 27
Neuro-symbolic AI for auditable cognitive information extraction from medical reports
Prenosil, G., Weitzel, T., Bello, S. C., Mingels, C., Manzini, G., Meier, L. P., Shi, K., Rominger, A., & Afshar‐Oromieh, A. (2025) · Communications Medicine · doi:10.1038/s43856-025-01194-x · Cited by 34
Accelerating Science with Human+AI Review
Manrai, A. K., Ouyang, D., Hogan, J. W., & Kohane, I. S. (2025) · NEJM AI · doi:10.1056/aie2501175 · Cited by 17
Self-citationKwan, H. Y., Shell, J., Fahy, C., Yang, S., & Xing, Y. (2025) · Systems · doi:10.3390/systems13040281 · Cited by 18
GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions
Noda, R., Tanabe, K., Ichikawa, D., & Shibagaki, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-99774-3 · Cited by 17
“To better assess these technologies in clinical settings, it is crucial to consider scenarios where physicians utilize them 16,17 .”
AI Agents, Automaticity, and Value Alignment in Health Care
Taylor, R. A. (2025) · NEJM AI · doi:10.1056/aip2401165 · Cited by 15
Applications of artificial intelligence‐driven microfluidics in medical laboratory science
Jiang, Q., Mo, Q., Ge, C., Li, W., Mai, J., Chen, Y., Liu, Y., Deng, X., Yang, Z., & Wang, D. (2025) · Interdisciplinary medicine · doi:10.1002/inmd.20240135 · Cited by 14
Scaling medical AI across clinical contexts
Li, M. M., Reis, B. Y., Rodman, A. M., Cai, T., Dagan, N., Balicer, R. D., Loscalzo, J., Kohane, I. S., & Žitnik, M. (2026) · Nature Medicine · doi:10.1038/s41591-025-04184-7 · Cited by 24
“The impact of context switching depends on the model’s design, who develops it and whose context it reflects 114 .”
Self-citationBaumgart, D. C., Cheng, C. H., Du, T. X., Parkes, M., Sadowski, D. C., Wine, E., Hoentjen, F., Halloran, B. P., Montaño‐Loza, A. J., Zepeda-Gómez, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01504-6 · Cited by 11
Right Care, Right Place, First Time: How AI Is Improving National Virtual Front Doors
McMahon, B., & McInerney, D. (2025) · NEJM AI · doi:10.1056/aipc2401260 · Cited by 11
Masukume, G. (2024) · medRxiv · doi:10.1101/2024.05.31.24308296 · Cited by 11
Moon, J., Jadhav, P., & Choi, S. T. (2025) · Journal of Rheumatic Diseases · doi:10.4078/jrd.2024.0128 · Cited by 11
“…research findings or changes to clinical criteria for diagnosis and disease activity emerge, DL models must be modified and revalidated to ensure continued reliability and effectiveness [148]. depending on how data is selected, weighted, and analyzed, so human decision-making remains vital [150].”
Evidence-Based Medicine: Past, Present, Future
Triposkiadis, F. K., & Brutsaert, D. L. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14145094 · Cited by 25
Evaluating the o1 reasoning large language model for cognitive bias: a vignette study
Degany, O., Laros, S., Idan, D., & Einav, S. (2025) · Critical Care · doi:10.1186/s13054-025-05591-5 · Cited by 10
“…as logical, objective tools-they are, upon reflection, consistent with how these models are developed: by learning from data and information generated by humans and through human-supervised learning methods, LLMs may inherit not only medical knowledge but also errors in human reasoning [16, 17].”
Medical Artificial Intelligence and Human Values
Manrai, A. K., Yu, K., & Kohane, I. S. (2024) · New England Journal of Medicine · doi:10.1056/nejmc2408971 · Cited by 10
Self-citationDontchos, B. N., Dodelzon, K., Bhole, S., Edmonds, C. E., Mullen, L. A., Parikh, J. R., Daly, C., Epling, J. A., Christensen, S., & Grimm, L. J. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.05.001 · Cited by 9
Makhlouf, H. R., Ossandon, M. R., Farahani, K., Lubensky, I. A., & Harris, L. N. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100531 · Cited by 9
Relational Ethics and Structural Epistemic Injustice of AI in Medicine
Herzog, C., & Branford, J. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00987-1 · Cited by 11
“Some authors have argued for explainable AI as a prerequisite for incorporating value-based arguments into the (shared) medical decision-making process (Bjerring & Busch, 2021; Herzog, 2022b; McDougall, 2019; Yu et al., 2024).”
Lupton, D. A., & Butler, E. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4871308 · Cited by 8
Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation
Huang, R., Meng, X., Zhang, X., Luo, Z., Cao, L., Feng, Q., Ma, G., Dong, D., & Wang, Y. (2025) · Interdisciplinary medicine · doi:10.1002/inmd.20240063 · Cited by 8
Toal, M. P., Hill, C. J., Quinn, M., O’Neill, C., & Maxwell, A. P. (2025) · Journal of Medical Internet Research · doi:10.2196/73603 · Cited by 8
“It is important that LLM outputs align with human values, which can be achieved through supervised reinforcement learning with input from expert physicians and patients [15].”
Erdat, E. C., & Şenler, F. Ç. (2025) · BMC Medical Ethics · doi:10.1186/s12910-025-01249-7 · Cited by 7
“However, the effective integration of AI into routine oncology practice requires not only robust technologies but also clinicians who are equipped with the requisite knowledge, confidence, and ethical awareness to navigate this evolving landscape [3].”
The missing value of medical artificial intelligence
Kohane, I. S., & Manrai, A. K. (2025) · Nature Medicine · doi:10.1038/s41591-025-04050-6 · Cited by 7
Self-citationContrastive learning enhances fairness in pathology artificial intelligence systems
Lin, S.-Y., Tsai, P.-C., Su, F.-Y., Chen, C.-Y., Li, F., Zhao, J., Ho, Y. Y., Lee, M. T., Healey, E., Lin, P.-J., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102527 · Cited by 13
Self-citationThe role of artificial intelligence in emergency medicine pharmacy practice
Edwards, C. J., Erstad, B. L., & Ng, V. (2025) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaf038 · Cited by 6
Artificial intelligence virtual extracellular vesicles (AIVEVs)
Liu, H., Li, S., Wang, J., & Su, J. H. (2026) · Bioactive Materials · doi:10.1016/j.bioactmat.2025.12.050 · Cited by 10
“Meanwhile, the rise of artificial intelligence (AI) has also significantly advanced computational biology [9,10].”
Lin, X., Zhang, Z., Zhou, T., Li, J., Jin, Q., Li, Y., Guan, Y., Xia, Y., Zhou, X., & Fan, L. (2025) · International Journal of COPD · doi:10.2147/copd.s508775 · Cited by 5
“113,114 Further, AI models require certain development and maintenance costs, and AI systems need to continuously update data and algorithms.”
InfluentialIversen, E., Ihle‐Hansen, H., Halle, K. K., Lundervold, A. S., Myrmel, L., Vestbø, A. S., Fromm, A., Autenried, C., & Brattebø, G. (2025) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2025.04.028 · Cited by 5
A multi-model longitudinal assessment of ChatGPT performance on medical residency examinations
Souto, M. E. V. C., Fernandes, A. C., da Silva, A. E. C., Ribeiro, L. H. D. F., & Fernandes, T. A. A. D. M. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1614874 · Cited by 5
Patient safety in AI-powered diagnostic pathology
Rugge, M., Fraschini, M., Orvieto, E., Didaci, L., Sandonà, L., D’Amuri, A., Saba, L., & Faa, G. (2025) · Journal of Clinical Pathology · doi:10.1136/jcp-2025-210231 · Cited by 5
Artificial Intelligence and Youth: Cognitive, Educational, and Behavioral Impacts
Giansanti, D., & Cosenza, C. (2026) · AI · doi:10.3390/ai7040121 · Cited by 11
Toward artificial general intelligence in health care
Ren, H., Kwok, Q., Sun, M., Huang, X., Zhu, J., & Li, H. (2025) · The Visual Computer · doi:10.1007/s00371-025-03808-w · Cited by 5
PSMA-Targeted radiopharmaceuticals: from diagnosis to risk & quality of life
Evangelista, L., Mansi, L., & Spadafora, M. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07507-5 · Cited by 4
Silik, O. E. (2025) · Asian Journal of Ethics in Health and Medicine · doi:10.51847/obxojlddeh · Cited by 4
Empowering the future of spinal surgery through digital and intelligent technologies
Gao, C., Yao, T., Zhang, T., Zhang, W., Wang, J., Zang, F., & Chen, H. (2025) · Spine Research · doi:10.1097/br9.0000000000000003 · Cited by 4
The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease
Holt, N., & Byrne, M. F. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.09.004 · Cited by 4
Pandya, S. K., Patel, C., Sojitra, B., Patel, J., Shah, P., & Shah, A. (2024) · Cureus · doi:10.7759/cureus.73948 · Cited by 6
Kohane, I. S. (2024) · medRxiv · doi:10.1101/2024.09.27.24314486 · Cited by 4
“As we contemplate the impact of AI-augmented clinical care, we must consider which of our values, if any, are represented in the AI programs providing this augmentation[4].”
BackgroundSelf-citationA protein-based classifier for differentiating follicular thyroid adenoma and carcinoma
Sun, Y., Wang, H., Lu, L., Wang, J., Chen, W., Peng, L., Hu, P., Yu, J., Cai, X., Yao, N., et al. (2025) · EMBO Molecular Medicine · doi:10.1038/s44321-025-00242-2 · Cited by 3
Transfer Learning-Based Super-Resolution for High-Precision Medical Imaging
Lee, J. Y., Hussain, M. I., Lee, K.-H., Shim, H.-S., Han, S., & Yang, D. (2025) · IEEE Access · doi:10.1109/access.2025.3587263 · Cited by 3
“Rapid advancements in Artificial Intelligence (AI) technologies have significantly impacted various industries, with the medical field being one of the primary beneficiaries [1], [2], [3], [4], [5].”
Marques‐Cruz, M., Sousa‐Pinto, B., Wiercioch, W., Reinap, M., Neumann, I., Chi, Y., Nowak, A. J., Awad, M., Nothacker, M., Brożek, J., et al. (2025) · Clinical and Public Health Guidelines · doi:10.1002/gin2.70038 · Cited by 3
“This is all more important considering that human values and biases are able to directly or indirectly influence the output of AI ‐ based models [12].”
Meng, H., Tang, Y., Qi, Y., Zhou, Q., He, L., & Chen, S. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1638904 · Cited by 3
He, R., Jie, P., Wang, Z., Lu, Y., Lv, H., Huang, Z., Liu, W., Wang, Y., Liu, W., Lei, W., et al. (2026) · Annals of Medicine · doi:10.1080/07853890.2026.2620335 · Cited by 3
Safety and security of large language models in healthcare
Clusmann, J., Freyer, O., Ostermann, M., Ferber, D., Laleh, N. G., Hilgers, L., Kolbinger, F. R., Schneider, C. V., Downing, A., Wekenborg, M., et al. (2026) · Nature · doi:10.1038/s41586-026-10687-1 · Cited by 5
“Through LLMs, institutions and LLM providers could impose their own goals on a process that was previously out of their reach 137 .”
A Perspective on Artificial Intelligence for Molecular Pathologists
O’Leary, T. J., O'Leary, B. J., & O’Leary, D. P. (2025) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2025.01.005 · Cited by 4
Canada and the EU united for global health, but challenges remain
Baumgart, D. C., Schneider, N. K., Lauterbach, K. W., & Lennerz, J. K. (2025) · The Lancet · doi:10.1016/s0140-6736(25)00191-6 · Cited by 3
Artificial Intelligence in Periodontology: The Past, the Present, the Future
Giannobile, W. V. (2025) · Journal of Periodontal Research · doi:10.1111/jre.13420 · Cited by 3
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
Potential roles of histone deacetylases in diabetic wound healing
Zhang, F., Ma, H., Zhang, B., Jiang, L.-L., Nie, K., Deng, C., & Liu, Y. (2025) · World Journal of Diabetes · doi:10.4239/wjd.v16.i10.108346 · Cited by 3
A diagnostic protein assay for differentiating follicular thyroid adenoma and carcinomaPreprint
Sun, Y., Wang, H., Li, L., Wang, J., Chen, W., Peng, L., Hu, P., Yu, J., Cai, X., Yao, N., et al. (2024) · medRxiv · doi:10.1101/2024.09.26.24314403 · Cited by 3
Generative Artificial Intelligence in Nutrition: A Revolution in Accessibility and Personalization
Pugliese, N., & Ravaioli, F. (2025) · Journal of Nutrition · doi:10.1016/j.tjnut.2025.01.025 · Cited by 4
Wei, Y., Mei, Z., Xie, C., Yuan, F., & Xu, D. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003828 · Cited by 2
Ethical considerations in the use of artificial intelligence in clinical neuropsychology
Bush, S. S. (2026) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2609770 · Cited by 3
“Incorporating human values into AI models from the initial model development stage may help to reduce bias in the models (yu et al., 2024).”
Zeng, G., Li, W., Mei, H., & Du, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1775750 · Cited by 2
Advances in artificial intelligence in prostate cancer pathology
Chan, G. J., & Ding, C. C. (2026) · Seminars in Diagnostic Pathology · doi:10.1016/j.semdp.2026.150995 · Cited by 2
Zheng, Q., Mei, H., Wang, D., Liu, X., Wang, L., & Chen, Z. (2026) · Annals of Surgical Oncology · doi:10.1245/s10434-026-19471-x · Cited by 2
Ubuntu, generative AI and machine-mediated relationships in South Africa
Naidoo, R. (2026) · South African Journal of Philosophy · doi:10.1080/02580136.2026.2645251 · Cited by 4
Pillai, S. S., & Ashraf, A. P. (2026) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000001564 · Cited by 2
Inteligencia artificial en la práctica clínica: calidad y evidencia
Puchades, R., & Ramos-Ruperto, L. (2024) · Revista Clínica Española · doi:10.1016/j.rce.2024.07.009 · Cited by 2
Camacho-Cogollo, J. E., Zambrano, C. F. P., Lochmuller, C., Colmenares-Mejía, C., Rozo, N., Isaza-Ruget, M., Rodriguez, P., & Garcia, A. (2025) · Healthcare Analytics · doi:10.1016/j.health.2024.100381 · Cited by 1
Zhang, L., Zhou, Y., Yang, S., Zhu, Q., Xu, J., Yuan, M., Gu, C., Ju, H., Rong, R., & Pan, S. (2025) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2024-0878 · Cited by 2
Tran, H. H.-V., Thu, A., Twayana, A. R., Fuertes, A., Gonzalez, M., Basta, M., James, M., Frishman, W. H., & Aronow, W. S. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000001042 · Cited by 2
Die Zukunft der Medizin: ein informierter Blick in die „Glaskugel“
Grgic, I. (2024) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/a-2410-9532 · Cited by 1
Studying the Role of Medicine in Nazism and the Holocaust to Shape Professional Identity Formation
Wald, H. S., & Ehrenfeld, J. M. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005988 · Cited by 1
Dıgıtal transformatıon ın healthcare: global best practıces and Kazakhstan’s path forward
Qumar, A. B., Faizullina, K., Seiduanova, L., Yelibay, A., Zhamankulova, N., & Mukashev, N. (2025) · Central Asian Journal of Medical Hypotheses and Ethics · doi:10.47316/cajmhe.2025.6.2.06 · Cited by 2
Harnessing Large Language Models to Promote Attainment of ACGME Competencies in Child Psychiatry
Sidamon‐Eristoff, A. E., Safranek, C. W., Martin, A., & Chartash, D. (2025) · JAACAP Connect · doi:10.62414/001c.141397 · Cited by 1
Yokoe, T., Roversi, G., Sevivas, N., Kamei, N., Diniz, P., & Pereira, H. (2025) · Journal of Experimental Orthopaedics · doi:10.1002/jeo2.70393 · Cited by 1
Residual disease in NPM1-mutated acute myeloid leukemia
Hamedi-Asl, P., Hosseinkhani, A., Ata-abadi, N. S., Ranjbar, A., Seyedebrahimi, H. S., Hoseinnezhad, T., Zahedi, P., Jafari, D., & Safa, M. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120586 · Cited by 1
Glioma drug development benefits from emerging phase 0 and window-of-opportunity trial paradigm
Jiang, S., & Zhong, C. (2025) · Cell Communication and Signaling · doi:10.1186/s12964-025-02408-4 · Cited by 1
“First of all, artificial intelligence (AI)-driven predictive modeling can optimize patient selection and dose-escalation strategies reducing trial duration and enhancing success rate [88, 89].”
Chen, S., Li, Z., Bigdon, S. F., Häckel, S., Albers, C. E., & Gantenbein, B. (2025) · JOR Spine · doi:10.1002/jsp2.70136 · Cited by 1
Cai, C., Zhang, J., Pan, C., Drew, Z. J., Wang, X., Dai, Q., Chen, G., & Li, K. (2025) · Discover Oncology · doi:10.1007/s12672-025-03992-0 · Cited by 1
“AI algorithms are particularly effective at identifying complex patterns in multidimensional datasets such as medical imaging, genomic data, and electronic health records [2–4].”
Artificial intelligence in geriatric medicine: potential and challenges. Systematic review
Martynenko, A. (2025) · Russian Journal of Geriatric Medicine · doi:10.37586/2686-8636-4-2025-557-570 · Cited by 1
Ultrasound-based artificial intelligence for breast lesion classification
Ma, T., Wang, Z., Dong, J., Cheng, Y., Zhao, H., & Cui, X. W. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1759194 · Cited by 3
Klimt’s ‘medicine’: at the crossroads of fate, healing, and human progress
Mehra, M. R., VUKIĆEVIĆ, M., & Zuckermann, A. O. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf1097 · Cited by 1
Quo Vadis translational neuroscience?
Dere, E. (2026) · Translational Neuroscience · doi:10.1515/tnsci-2025-0393 · Cited by 1
A Hybrid Ensemble Framework for Rare Event Detection in Large-Scale Tabular Data
Maxutova, N., Kassymova, А., Kadirkulov, K., Ismailova, A., Zhidekulova, G., Azhibekova, Z., Tussupov, J., Rakhimov, Q., & Kenzhebayeva, Z. (2026) · Computers · doi:10.3390/computers15030151 · Cited by 1
Chen, Z., Li, W., & Lin, Z. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001283 · Cited by 5
“From this perspective, even when AI does not yield statistically superior gains for minority or economically disad-vantaged groups, its ability to deliver results that are not significantly worse than human practitioners remains clinically meaningful [45,59].”
Semba: A semantic–visual fusion method based on MLLM and Mamba for IBD subtype identification
Wang, J., Ruan, G., Hualong, L., Nian, Y., Wei, Y., & Lei, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110358 · Cited by 1
Chen, Z., Shu, J., Wang, D., Erlian, S. U. N., Wang, B., Qi, L., Zhang, H., Han, S., Wu, Y., & Wang, G. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106476 · Cited by 3
Zhao, X., Ma, L. L., Li, P., Hu, Z., Ding, Y. P., Liu, Q. F., & Liu, K. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1842344 · Cited by 1
Artificial Intelligence in Practice: BCBAs’ Attitudes, Concerns, and Needs
Meadan, H., Alqunaysi, R., Xia, Y. R., Loya, M. R. M., & Xiong, J. (2026) · Education and Treatment of Children · doi:10.1007/s43494-026-00180-9 · Cited by 1
Current Trends in AI Ethics for Software as a Medical Device (SaMD)
Vu, T., & Throne, R. (2024) · Advances in data mining and database management book series · doi:10.4018/979-8-3693-3848-3.ch006 · Cited by 1
Kunstig intelligens kan ikke utføre kvalitativ analyse
Malterud, K. (2025) · Tidsskrift for Den norske legeforening · doi:10.4045/tidsskr.24.0571 · Cited by 1
Metadata
- Authors
- Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2024-05-30
- Volume
- 390
- Issue
- 20
- Pages
- 1895-1904
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W4399120295
- Cited by
- 149
- References
- 70
- Citations by year
- 2026: 47 · 2025: 85 · 2024: 17
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL174679)
- National Institute of General Medical Sciences (R35GM142879)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- large language models, generative AI, human values, clinical decision-making, algorithmic decision-making, AI safety, foundation models, clinical decision support
- MeSH terms
- Clinical Decision-Making, Clinical Reasoning, Artificial Intelligence, Humans, Language, Risk, Social Values, Bias
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
