The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Abstract
Chatbots are computer programs with which one can have a conversation. In this article, the authors describe how the GPT-4 chatbot, which has been given a general education, could affect the practice of medicine.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Large language models (LLMs), with their superior contextual semantic understanding, offer a promising avenue to overcome these limitations [8, 9].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [8]
In the citing paper
“Previous research has documented LLM hallucination in various contexts, including medical information [8], legal reasoning [9], and general factual queries [10].”
From Compound Deception in Elite Peer Review: A Failure Mode Taxonomy of 100 Fabricated Citations at NeurIPS 2025 (2026) · cites this paper as [8]
In the citing paper
“This is in contrast to prior work that claims LMs approach human performance, even for complex tasks (Ding et al., 2022; Nori et al., 2023; Lee et al., 2023) even though the task of writing biographies is fairly easy.”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Lee et al., 2023
In the citing paper
“This is in contrast to prior work that claims LMs approach human performance, even for complex tasks (Ding et al., 2022; Nori et al., 2023; Lee et al., 2023) even though the task of writing biographies is fairly easy.”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Lee et al., 2023
In the citing paper
“Studies evaluating GPT-4 and Claude on medical board examinations report accuracy rates of 80-90% [3,6]”
From Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware (2026) · cites this paper as [3]
In the citing paper
“Large Language Models (LLMs) demonstrating remarkable capabilities in medical knowledge synthesis, diagnostic reasoning, and clinical documentation [1-3].”
From Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware (2026) · cites this paper as [3]
In the citing paper
“Most published medical AI benchmarks employ enterprise GPUs (NVIDIA A100, H100) or cloud API access, reporting neither hardware specifications nor inference costs [2,3,7].”
From Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware (2026) · cites this paper as [3]
In the citing paper
“While these models offer powerful foundational capabilities, adapting them to specific tasks and datasets without losing robustness is a significant hurdle, often resulting in significant variability in accuracy across different settings.7,9,10”
In the citing paper
“Despite their ability to create novel and customized content, these models are prone to hallucinations7 and can produce factually incorrect yet plausible outputs that may mislead users.”
In the citing paper
“If generative AI in health care is used for diagnostics, personalized medicine, or clinical assistance, AI hallucination can be extremely dangerous and may even harm patients’ lives [83].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [83]
In the citing paper
“However, their clinical deployment carries substantial limitations, including the generation of inaccurate or fabricated outputs (commonly termed “hallucinations”), variable output quality, and the absence of transparent reasoning pathways [4].”
In the citing paper
“Without full knowledge of the intervention being applied, it is difficult to interpret LLM performance.9,17,156”
From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [9]
Citations
Cites
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083
Deep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,574
A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning
Han, K., Cao, P., Wang, Y., Xie, F., Ma, J., Yu, M., Wang, J., Xu, Y., Zhang, Y., & Wan, J. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.814858 · Cited by 180
Beaulieu‐Jones, B. K., Yuan, W., Brat, G. A., Beam, A. L., Weber, G. M., Ruffin, M., & Kohane, I. S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00426-3 · Cited by 168
Comparison of biomedical relationship extraction methods and models for knowledge graph creation
Milošević, N., & Thielemann, W. (2022) · Journal of Web Semantics · doi:10.1016/j.websem.2022.100756 · Cited by 49
The US Medical Licensing Examination
Cancarevic, I. (2021) · Springer eBooks · doi:10.1007/978-3-030-62249-7_23 · Cited by 2
Research works printed but not linked: 1
- Corbelle JG Bugarín-Diz A Alonso-Moral J Taboada J. Dealing with hallucination and omission in neural Natural Language Generation: a use case on meteorology. In: Proceedings and Abstracts of the 15th International Conference on Natural Language Generation July 18–22 2022. Waterville ME: Arria 2022.
Cited by
Sorted by citations
Items 801–900 of 1,012
Solmonovich, R. L., Kouba, I., Lee, J. Y., Demertzis, K. H., & Blitz, M. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320749 · Cited by 10
Patel, K., Connor, A. A., Kodali, S., Mobley, C. M., Victor, D., Hobeika, M. J., Dib, Y. E., Saharia, A., Cheah, Y. L., Simon, C. J., et al. (2025) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.103 · Cited by 8
McBride, E. R., Leung, E. W., & Ford, J. C. (2025) · Vox Sanguinis · doi:10.1111/vox.70070 · Cited by 7
Genovese, A., Prabha, S., Gomez-Cabello, C. A., Haider, S. A., Borna, S., Trabilsy, M., & Forte, A. J. (2025) · Journal of surgical education · doi:10.1016/j.jsurg.2025.103622 · Cited by 7
Rapid deployment of large language model DeepSeek in Chinese hospitals demands a regulatory response
Shen, T., Li, Y., Cao, Y., Du, X., Wang, X., Zhang, Y., & Zhang, Y. J. (2025) · Nature Medicine · doi:10.1038/s41591-025-03836-y · Cited by 7
Vedder, K., Blank, S., Wilhelm, T., Fidan, D., Pachkiv, I., Cao, H., Weiß, C., Li, C., Rung-Friebe, M., Reißfelder, C., et al. (2025) · Obesity Surgery · doi:10.1007/s11695-025-08115-w · Cited by 7
Zamora, T., Salas, P., Zuñiga, S., Botello, E., & Andía, M. E. (2025) · Journal of Clinical Orthopaedics and Trauma · doi:10.1016/j.jcot.2025.103161 · Cited by 7
GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction
Samadi, M. E., Nikulina, K., Fritsch, S. J., & Schuppert, A. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03224-z · Cited by 8
You, Q., Luo, Y., Chen, Z., Song, X., Wang, F., Wen, S. W., Ding, Q., & Chen, J. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104668 · Cited by 8
Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework
Santos, J. F., & Dores, H. (2026) · Diagnostics · doi:10.3390/diagnostics16030390 · Cited by 8
GPT4: The Indispensable Helper for Neurosurgeons in the New Era
Liu, G., Ma, X., Zhang, Y., Su, B., & Liu, P. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03241-x · Cited by 7
The application of chatbot in gastroenterology nursing
Zhao, Y., Gao, C., Zhang, L., Gao, X., & Zhang, Z. (2023) · Gastroenterology & Endoscopy · doi:10.1016/j.gande.2023.07.003 · Cited by 7
Ketchem, C. J., Lynch, K. L., Chang, J. W., & Dellon, E. S. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2023.10.004 · Cited by 7
Evaluating AI performance in answering questions related to thoracic anatomy
Mavrych, V., & Bolgova, O. (2023) · MOJ Anatomy & Physiology · doi:10.15406/mojap.2023.10.00339 · Cited by 7
Revolutionizing healthcare through Chat GPT: AI is accelerating medical diagnosis
Kharat, P. B., Dash, K. S., Rajpurohit, L., Tripathy, S., & Mehta, V. (2024) · Oral Oncology Reports · doi:10.1016/j.oor.2024.100222 · Cited by 7
Feasibility and Challenges of Interactive AI for Traditional Chinese Medicine: An Example of ChatGPT
Kong, Q., Chen, L. Y., Jingyi, Y. A. O., Ding, C., & Yin, P. (2024) · Chinese Medicine and Culture · doi:10.1097/mc9.0000000000000103 · Cited by 8
Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning
Liu, M. X., Petridis, S., Tsai, V., Fiannaca, A. J., Olwal, A., Terry, M. A., & Cai, C. J. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712085 · Cited by 7
Karaçay, P., Göktaş, P., Yaşar, Ö., Uyanik, B., Uzlu, S., Coşkun, K., & Benk, M. (2025) · Journal of Advanced Nursing · doi:10.1111/jan.16905 · Cited by 10
Rossi, N. A. A., Corona, K. K., Yoshiyasu, Y., Hajiyev, Y., Hughes, C. A., & Pine, H. S. (2025) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2025.112336 · Cited by 9
The performance of ChatGPT and ERNIE Bot in surgical resident examinations
Guo, S., Li, G., Du, W. D., Situ, F., Zhi-hui, L., & Lei, J. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105906 · Cited by 7
Accuracy and Reproducibility of ChatGPT Responses to Breast Cancer Tumor Board Patients
Liao, N., Li, C., Gradishar, W. J., Klimberg, V. S., Roshal, J. A., Yuan, T., Agarwala, S. S., Valero, V. K., Swain, S. M., Margenthaler, J. A., et al. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00001 · Cited by 7
Aydilek, A., & Karadamar, Ö. L. (2025) · Journal of orthopaedic surgery · doi:10.1177/10225536251350411 · Cited by 8
Artificial intelligence in cardiology: Exciting but handle with caution
Dunn, P. J., & Cianflone, D. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.06.008 · Cited by 7
Let's Look on the Bright Side of ChatGPT
Yoo, J. (2023) · Journal of Korean Medical Science · doi:10.3346/jkms.2023.38.e231 · Cited by 8
L-AutoDA: Large Language Models for Automatically Evolving Decision-based Adversarial Attacks
Guo, P., Liu, F., Lin, X., Zhao, Q., & Zhang, Q. (2024) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3638530.3664121 · Cited by 9
Ritz, T. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108590 · Cited by 9
The Role of Large Language Models in Medical Education: Applications and Implications (Preprint)Preprint
Safranek, C. W., Sidamon‐Eristoff, A. E., Gilson, A., & Chartash, D. (2023) · doi:10.2196/preprints.50945 · Cited by 7
Meaney, C. A., Huang, R. S., Lu, K. J. Q., Fischer, A. W., Leung, F., Kulasegaram, K. M., Tzanetos, K., & Punnett, A. (2023) · medRxiv · doi:10.1101/2023.09.14.23295571 · Cited by 7
Evaluating the use of GPT-3.5-turbo to provide clinical recommendations in the Emergency DepartmentPreprint
Williams, C. Y. K., Miao, B. Y., & Butte, A. J. (2023) · medRxiv · doi:10.1101/2023.10.19.23297276 · Cited by 7
Assessing the research landscape and clinical utility of large language models: A scoping reviewPreprint
Park, Y., Pillai, A., Deng, J., Guo, E., Gupta, M., Paget, M., & Naugler, C. T. (2023) · Research Square · doi:10.21203/rs.3.rs-3472000/v1 · Cited by 8
A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?Preprint
Mei, Q., Xie, Y., Yuan, W., & Jackson, M. O. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4637354 · Cited by 7
Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor AgonistsPreprint
Bartal, A., Jagodnik, K. M., Pliskin, N., & Seidmann, A. A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4790676 · Cited by 8
Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence
Kavanagh, K. T., Pontus, C., & Cormier, L. E. (2024) · Journal of Patient Safety · doi:10.1097/pts.0000000000001245 · Cited by 6
Guo, Z., Lai, A. G., Deng, Z., & Li, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67278-1_20 · Cited by 6
Nittas, V., Ormond, K. E., Vayena, E., & Blasimme, A. (2025) · BMC Cancer · doi:10.1186/s12885-025-13621-2 · Cited by 9
The Misinformation Risks of Generative AI in Health Care: A Patient-centered Perspective
As’ad, M., & Faran, N. (2025) · Journal of Patient Safety · doi:10.1097/pts.0000000000001329 · Cited by 6
Rouck, R. D., Wille, E., Gilbert, A., & Vermeersch, N. (2025) · International Journal of Emergency Medicine · doi:10.1186/s12245-025-00885-5 · Cited by 9
Chuang, W.-K., Kao, Y., Liu, Y., & Lee, C.-Y. (2025) · Practical Radiation Oncology · doi:10.1016/j.prro.2025.04.009 · Cited by 6
Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study
Pillai, A., Joseph, S. P., Kreutz, J., Traboulsi, D., Gandhi, M., & Hardin, J. (2025) · Journal of Cutaneous Medicine and Surgery · doi:10.1177/12034754251336238 · Cited by 16
Lee, J., Oh, S. E., Schwendicke, F., Chaurasia, A., & Kim, Y. (2025) · Journal of Periodontal & Implant Science · doi:10.5051/jpis.2402220111 · Cited by 6
How well does GPT-4 perform on an emergency medicine board exam? A comparative assessment
Almehairi, N., Clark, G., & Davis, S. N. P. (2025) · Canadian Journal of Emergency Medicine · doi:10.1007/s43678-025-00951-0 · Cited by 6
Jung, J., Phillipi, M., Tran, B., Chen, K., Chan, N., Ho, E., Sun, S. H., & Houshyar, R. (2025) · Pediatric Radiology · doi:10.1007/s00247-025-06317-z · Cited by 6
Chen, Q., Long, P., Dong, R., Li, C., & Mao, S. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1549283 · Cited by 8
Integrated Biomarker Separation and Detection with Carbon Nanotubes: Advances and Prospects
Liu, Y., Zhang, J., Chen, X., Ren, L., & Wang, M. (2025) · Small Methods · doi:10.1002/smtd.202500117 · Cited by 6
ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering
Gichamba, A., Idris, T. K., Ebiyau, B., Nyberg, E., & Mitamura, T. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100297 · Cited by 10
Domián, B. M., Ashraf, A. R., Fittler, A. T., Káplár, M., & Vida, R. G. (2025) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2025.100655 · Cited by 6
Christopher, C., Pathak, N., Babar, Z.-U.-D., & Shrestha, S. (2025) · Aging Medicine · doi:10.1002/agm2.70051 · Cited by 5
Clinical pathway-aware large language models for reliable and transparent medical dialogue
Wu, J., Wu, X., Zheng, Y., & Yang, J. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104942 · Cited by 10
Assessing the User Experience of an LLM-Based Conversational Assistant in Diabetes Mellitus Care
Furtado, V., Araujo, J., Furtado, E. S., Bomfim, R., Silva, V., Mororo, N. A. C., Vasconcelos, L., Carvalho, C., Severo, M., Torres, J., et al. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00217-5 · Cited by 7
Li, S., Fang, X., Jin, Y., Deng, Y., Hu, W., Wu, B., Zhou, X., Wang, G., Li, K., & Yue, Q. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01171-6 · Cited by 7
Performance of Large Language Models in Metabolic Bariatric Surgery: a Comparative Study
El-Masry, H., El‐mezayen, M. Y., Farouk, B., Tawfik, A. M., Sharsher, P. S., Mohamed, B. H., Elmagd, A. A., Khammas, A. A., Nimeri, A. A., COHEN, R. V., et al. (2025) · Obesity Surgery · doi:10.1007/s11695-025-08418-y · Cited by 6
Luo, J., Zhang, Y., Zhang, Z., Liu, S., Dong, Y., Li, H., Yu, Y. S., Wang, H., Zhou, X., & Xu, Z. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11466-6 · Cited by 8
Motevalli, M. S., Boaventura, B. C. B., & Stanford, F. C. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.100980 · Cited by 9
The future of commercial genetic testing
Solomon, B. D. (2023) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000001260 · Cited by 6
Generative pretrained transformer 4: an innovative approach to facilitate value-based healthcare
Lv, H., Wang, Z., Li, J., Sun, J., Wang, X., Ren, P., Cai, L., Wang, Z., & Wintermark, M. (2023) · Intelligent Medicine · doi:10.1016/j.imed.2023.09.001 · Cited by 6
Online health communities provide important support in China
Zhang, Q. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01766-8 · Cited by 6
Accomodating User Expressivity while Maintaining Safety for a Virtual Alcohol Misuse Counselor
Ólafsson, S., Pedrelli, P., Wallace, B., & Bickmore, T. (2023) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3570945.3607361 · Cited by 6
Szczesniewski, J. J., Alba, A. R., Castro, P. M. R., Lorenzo-Gómez, M. F., González, J. P., & González, L. L. (2024) · Actas Urológicas Españolas · doi:10.1016/j.acuro.2023.12.002 · Cited by 7
Kaufmann, B., Busby, D., Das, C. K., Tillu, N., Menon, M., Tewari, A. K., & Gorin, M. A. (2024) · European Urology Focus · doi:10.1016/j.euf.2024.01.009 · Cited by 9
Leveraging OpenAI's Advanced Data Analysis Tool in Dermatology: Opportunities and Challenges
Jairath, N. K., Qiblawi, S. H., Jeha, G. M., Pahalyants, V., Jairath, R., Cheraghlou, S., Ramachandran, V., Xu, Y. G., & Aylward, J. L. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.01.023 · Cited by 6
Finelli, P. F. (2024) · The Neurologist · doi:10.1097/nrl.0000000000000560 · Cited by 6
Comparison of clinical geneticist and computer visual attention in assessing genetic conditions
Duong, D., Johny, A. R., Hanchard, S. E. L., Fortney, C., Flaharty, K. A., Hellmann, F., Hu, P., Javanmardi, B., Moosa, S., Patel, T., et al. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011168 · Cited by 6
GPT VS. HUMAN FOR SCIENTIFIC REVIEWS: A DUAL SOURCE REVIEW ON APPLICATIONS OF CHATGPT IN SCIENCE
Wu, C., Varghese, A. J., Oommen, V., & Karniadakis, G. E. (2024) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2024052432 · Cited by 8
Evaluation of a Large Language Model’s Ability to Assist in an Orthopedic Hand Clinic
Kotzur, T. M., Singh, A., Parker, J., Peterson, B. C., Sager, B. W., Rose, R., Corley, F. G., & Brady, C. I. (2024) · Hand · doi:10.1177/15589447241257643 · Cited by 6
Welches Potential hat ChatGPT 3.5 für eine qualifizierte Patienteninformation?
Keyßer, G., Pfeil, A., Reuss-Borst, M. A., Frohne, I., Schultz, O., & Sander, O. (2024) · Zeitschrift für Rheumatologie · doi:10.1007/s00393-024-01535-6 · Cited by 6
Zhang, G., Li, B., Gu, Z.-M., Yang, W., Wang, Y., Li, H., Zheng, H. L., Yue, Y.-X., Wang, K.-Z., Gong, M., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/54018 · Cited by 6
Malik, S., Frey, L. J., Gutman, J., Mushtaq, A., Warraich, F. Z., & Qureshi, K. (2024) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000003255 · Cited by 7
Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology
Alessandro, L., Crema, S., Castiglione, J. I., Dossi, D., Eberbach, F., Köhler, A., Laffue, A., Marone, A., Nagel, V., Rueda, J. M. P., et al. (2025) · The Neurologist · doi:10.1097/nrl.0000000000000594 · Cited by 6
Herbert, D. N., Westendorf, J., Farmer, M., & Reeder, B. (2025) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.24-00319 · Cited by 6
Assessing AI Accuracy in Generating CPT Codes From Surgical Operative Notes
Isch, E. L., Monzy, J., Thota, B., Somers, S., Self, D. M., & Caterson, E. J. (2025) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000011258 · Cited by 7
Xu, J., Wang, J., Li, J., Zhu, Z., Xiao, F., Cai, W., Song, R., Wang, T. F., & Li, H. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02192-1 · Cited by 9
Brean, A. (2023) · Tidsskrift for Den norske legeforening · doi:10.4045/tidsskr.23.0279 · Cited by 6
Assessing ChatGPT’s Capability for Multiple Choice Questions Using RaschOnline: Observational Study
Chow, J. C., Cheng, T.-Y., Chien, T., & Chou, W. (2023) · JMIR Formative Research · doi:10.2196/46800 · Cited by 6
Limitations of GPT-4 as a geriatrician in geri-oncology case conference: A case series
Kuk, L.-Y., Kwong, D. L., Chan, W., & Shea, Y. F. (2023) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000001032 · Cited by 6
AI's Secret Weapon in Education. ChatGPT – The Future of Personalized Learning
Popescu, A. (2024) · Bulletin of the Transilvania University of Brasov Series V Economic Sciences · doi:10.31926/but.es.2023.16.65.2.5 · Cited by 10
Application of large language models in professional fields
Zhou, M., Chen, W., Zhu, S., Cai, T., Yu, J., & Dai, G. (2023) · International Conference on Information Systems and Computing Technology (ISCTech) · doi:10.1109/isctech60480.2023.00033 · Cited by 6
Garg, S., & Chauhan, A. (2024) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2024.103972 · Cited by 6
Potential of Large Language Models as Tools Against Medical Disinformation—Reply
Hopkins, A. M., Menz, B. D., & Sorich, M. J. (2024) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2024.0023 · Cited by 6
Will ChatGPT soon replace otolaryngologists?
Mat, Q., Briganti, G., Maniaci, A., & Lelubre, C. (2024) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-024-08543-x · Cited by 6
The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey
Giannitto, C., Carnicelli, G., Lusi, S., Ammirabile, A., Casiraghi, E., VIRGILIO, A. D., Esposito, A. A., Farina, D., Ferreli, F., Franzese, C., et al. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14040341 · Cited by 6
Study of Tokenization Strategies for the Santhali Language
Ohm, A. K., & Singh, K. K. (2024) · SN Computer Science · doi:10.1007/s42979-024-03083-x · Cited by 6
Shiraishi, M., Tsuruda, S., Tomioka, Y., Chang, J., Hori, A., Ishii, S., Fujinaka, R., Ando, T., Ohba, J., & Okazaki, M. (2024) · Aesthetic Plastic Surgery · doi:10.1007/s00266-024-04377-4 · Cited by 6
Gill, K. S., Cacciamani, G. E., Nabhani, J. A., Corb, J., Buchanan, T., Park, D., Bhardwaj, V., Marwah, O., Kim, M., Kapoor, D. A., et al. (2023) · medRxiv · doi:10.1101/2023.06.26.23291879 · Cited by 6
ChatGPT and beyond with artificial intelligence (AI) in health: Lessons to be learned
Thiébaut, R., Hejblum, B. P., Fleur Mougin, A., Tzourio, C., & Richert, L. E. (2023) · Joint Bone Spine · doi:10.1016/j.jbspin.2023.105607 · Cited by 7
Diagnostic Accuracy of GPT Multimodal Analysis on USMLE Questions Including Text and VisualsPreprint
Sorin, V., Glicksberg, B. S., Barash, Y., Konen, E., Nadkarni, G. N., & Klang, E. (2023) · medRxiv · doi:10.1101/2023.10.29.23297733 · Cited by 6
Vithanage, D., Deng, C., Wang, L., Yin, M., Alkhalaf, M., Zhang, Z., Zhu, Y., Soewargo, A. C., & Yu, P. (2024) · medRxiv · doi:10.1101/2024.06.24.24309441 · Cited by 6
Improved precision oncology question-answering using agentic LLMPreprint
Das, R., Maheswari, K., Siddiqui, B. S., Arora, N., Paul, A., Nanshi, J., Udbalkar, V., Sarvade, A., Chaturvedi, H., Shvartsman, T., et al. (2024) · medRxiv · doi:10.1101/2024.09.20.24314076 · Cited by 6
Artificial Intelligence Large Language Models in Cardiology
Chen, J. M., Liang, Y., & Ge, J. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm39452 · Cited by 6
Ayaaba, E., Adusei‐Asante, K., Nunfam, V. F., Rumchev, K. B., & Amponsah, S. K. (2024) · Critical Public Health · doi:10.1080/09581596.2024.2438654 · Cited by 6
Naeem, A., Khan, O. A., Baqir, S. M., Jana, K. R., Shankar, P., Kaur, A., Zaaya, M., Sajid, F., Mohsin, F., Boadla, M. R., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14041131 · Cited by 5
A comparative analysis of large language models on clinical questions for autoimmune diseases
Chen, J., Ma, J., Yu, J., Zhang, W., Zhu, Y., Feng, J., Geng, L., Dong, X., Zhang, H., Chen, Y., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1530442 · Cited by 5
SCREADER: Prompting Large Language Models to Interpret scRNA-seq Data
Li, C., Long, Q., Zhou, Y., & Xiao, M. (2024) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw65004.2024.00092 · Cited by 7
D’Urso, F., Paladini, F., Pollini, M., & Broccolo, F. (2025) · Applied Sciences · doi:10.3390/app15084232 · Cited by 5
Muoka, C. G., Ewurum, O., Godwin, G. U., Nwoke-Udeka, I., Nnedimma, A. V., & Moneke, I. I. (2025) · International Journal of Research in Medical Sciences · doi:10.18203/2320-6012.ijrms20251617 · Cited by 6
Hao, W., Chen, C., Chen, K., Li, L., Chiu, C.-C., Yang, T.-Y., Jong, H.-C., Yang, H., Huang, C., Liu, J., et al. (2025) · Healthcare · doi:10.3390/healthcare13131598 · Cited by 6
Zeng, A., Steinke, J., Bocse, H., & Pastena, M. D. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14145101 · Cited by 5
ChatGPT and Microsoft Copilot for Cochlear Implant Side Selection: A Preliminary Study
Portelli, D., Loteta, S., D’Angelo, M., Galletti, C., Freni, L., Bruno, R., Ciodaro, F., Alibrandi, A., & Alberti, G. (2025) · Audiology Research · doi:10.3390/audiolres15040100 · Cited by 7
Application prospect of large language model represented by ChatGPT in ophthalmology
Kuang, J., Zeng, M.-B., Chen, F.-L., & Huang, W. (2025) · International Journal of Ophthalmology · doi:10.18240/ijo.2025.09.21 · Cited by 5
Feasibility of AI-powered assessment scoring: Can large language models replace human raters?
Jaworski, M. G., Balconi, J., Santivasci, C. A., & Calamia, M. R. (2025) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2552289 · Cited by 5
Egli, S. B., Arpagaus, A., Amacher, S. A., Hunziker, S., & Bassetti, S. (2025) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2025-101470 · Cited by 6
Metadata
- Authors
- Lee, P., Bubeck, S., & Petro, J.
- Affiliations
- Microsoft (United States) (US)
- From Microsoft Research, Redmond, WA (P.L., S.B.); and Nuance Communications, Burlington, MA (J.P.).
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2023-03-30
- Volume
- 388
- Issue
- 13
- Pages
- 1233-1239
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W4361289889
- Semantic Scholar
- 72b74bcff8fd76eff6789111a7ce5d0d6c5ac4db
- Cited by
- 1,738
- References
- 6
- Citations by year
- 2026: 405 · 2025: 419 · 2024: 542 · 2023: 370
- FWCI
- 69.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- GPT-4, large language models, AI chatbots, medical practice, clinical decision support, artificial intelligence in healthcare
- MeSH terms
- Artificial Intelligence, Humans, Medicine, Risk Assessment
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
