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Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format
Open accessAbstract
Importance: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the potential to transform these notes into patient-friendly language and format. Objective: To determine whether an LLM can transform discharge summaries into a format that is more readable and understandable. Design, Setting, and Participants: This cross-sectional study evaluated a sample of the discharge summaries of adult patients discharged from the General Internal Medicine service at NYU (New York University) Langone Health from June 1 to 30, 2023. Patients discharged as deceased were excluded. All discharge summaries were processed by the LLM between July 26 and August 5, 2023. Interventions: A secure Health Insurance Portability and Accountability Act-compliant platform, Microsoft Azure OpenAI, was used to transform these discharge summaries into a patient-friendly format between July 26 and August 5, 2023. Main Outcomes and Measures: Outcomes included readability as measured by Flesch-Kincaid Grade Level and understandability using Patient Education Materials Assessment Tool (PEMAT) scores. Readability and understandability of the original discharge summaries were compared with the transformed, patient-friendly discharge summaries created through the LLM. As balancing metrics, accuracy and completeness of the patient-friendly version were measured. Results: Discharge summaries of 50 patients (31 female [62.0%] and 19 male [38.0%]) were included. The median patient age was 65.5 (IQR, 59.0-77.5) years. Mean (SD) Flesch-Kincaid Grade Level was significantly lower in the patient-friendly discharge summaries (6.2 [0.5] vs 11.0 [1.5]; P < .001). PEMAT understandability scores were significantly higher for patient-friendly discharge summaries (81% vs 13%; P < .001). Two physicians reviewed each patient-friendly discharge summary for accuracy on a 6-point scale, with 54 of 100 reviews (54.0%) giving the best possible rating of 6. Summaries were rated entirely complete in 56 reviews (56.0%). Eighteen reviews noted safety concerns, mostly involving omissions, but also several inaccurate statements (termed hallucinations). Conclusions and Relevance: The findings of this cross-sectional study of 50 discharge summaries suggest that LLMs can be used to translate discharge summaries into patient-friendly language and formats that are significantly more readable and understandable than discharge summaries as they appear in electronic health records. However, implementation will require improvements in accuracy, completeness, and safety. Given the safety concerns, initial implementation will require physician review.
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“However, variability in output quality, including hallucinations and incomplete contextualization, necessitates human oversight.42”
From Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. (2026) · cites this paper as [42]
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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
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613
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
Shoemaker, S. J., Wolf, M. S., & Brach, C. (2014) · Patient Education and Counseling · doi:10.1016/j.pec.2014.05.027 · Cited by 1,422
Interventions to Reduce 30-Day Rehospitalization: A Systematic Review
Hansen, L. O., Young, R. S., Hinami, K., Leung, A., & Williams, M. V. (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00008 · Cited by 1,182
Greene, J., & Hibbard, J. H. (2011) · Journal of General Internal Medicine · doi:10.1007/s11606-011-1931-2 · Cited by 1,028
Do increases in patient activation result in improved self-management behaviors?
Hibbard, J. H., Mahoney, E. R., Stock, R., & Tusler, M. (2007) · Health Services Research · doi:10.1111/j.1475-6773.2006.00669.x · Cited by 906
Addressing bias in artificial intelligence in health care
Parikh, R. B., Teeple, S., & Navathe, A. S. (2019) · JAMA · doi:10.1001/jama.2019.18058 · Cited by 764
Readability Standards for Informed-Consent Forms as Compared with Actual Readability
Paasche‐Orlow, M. K., Taylor, H. A., & Brancati, F. L. (2003) · New England Journal of Medicine · doi:10.1056/nejmsa021212 · Cited by 763
Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., Chang, S., Berkowitz, S., Finn, A., Jahangir, E., et al. (2023) · doi:10.21203/rs.3.rs-2566942/v1 · Cited by 598
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- Weiss, 2003, Health Literacy: A Manual for Clinicians, 51
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Items 101–200 of 306
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Wang, Q., Gong, Z., Lai, Z., Bu, L., Dahlweid, F.-M., & Sun, H. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1772274 · Cited by 4
Generative AI in the Care of Older Adults: A Position Statement From the American Geriatrics Society
Bharija, A., Khan, A., Pavon, J. M., Hashmi, A. Z., Jindal, S. K., Dredze, M. H., Josyula, A. V., Hung, W., Nothelle, S. K., Patel, N. K., et al. (2026) · Journal of the American Geriatrics Society · doi:10.1111/jgs.70566 · Cited by 5
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Gao, Y., Xu, Q., Zhang, O., Wang, H., Wang, Y., Wang, J., & Chen, X. (2025) · Endocrine · doi:10.1007/s12020-025-04339-z · Cited by 8
Shen, N., Xu, C., Yang, Y., & Zhang, D. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1664542 · Cited by 8
Dullabh, P., Zott, C., Gauthreaux, N. A., Peterson, C., Aronoff, A., Monkhouse, K., & Sittig, D. F. (2026) · Journal of Medical Internet Research · doi:10.2196/81628 · Cited by 7
Evaluating LLMs for Diagnosis Summarization
Santos, J., dos Santos, H. D. P., Ulbrich, A. H. D. P. S., Faccio, D., Tabalipa, F. D. O., Nogueira, R. F., & Costa, M. M. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782231 · Cited by 5
MacKenzie, J., Coric, C., Kim, Y. W., Lee, C., & Grant-Kels, J. M. (2025) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2025.06.002 · Cited by 3
Griffin, A. C., Javier, S. J., Golding, M., Runnels, T. W., Matthias, M. S., Shimada, S. L., Higgins, D. M., Zulman, D. M., & Midboe, A. M. (2025) · Journal of Medical Internet Research · doi:10.2196/78173 · Cited by 4
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What is a “good” website for communicating biosecurity information to farmers and veterinarians?
Yıldız, R., Batikas, G., Iatrou, A. M., Allepuz, A., Mehmedi, B., Gecaj, R. M., Nardi, M. D., Toppari, I., Wielick, C., Saegerman, C., et al. (2025) · Research in Veterinary Science · doi:10.1016/j.rvsc.2025.105938 · Cited by 3
Nagesh, D., Keating, D. P., Divakaruni, R. V., & Beutel, B. G. (2025) · PEC Innovation · doi:10.1016/j.pecinn.2025.100444 · Cited by 3
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Verastegui, A., Castaneda, R., Gómez-Gutiérrez, O. A., González-Urquijo, M., la Torre, O. A. D., Vegas, D. H., Ysa, A., García-Toca, M., & Fabiani, M. A. (2025) · Vascular Medicine · doi:10.1177/1358863x251386394 · Cited by 3
Han, H. S., & Lee, J. (2026) · Kidney Medicine · doi:10.1016/j.xkme.2026.101247 · Cited by 3
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Menz, B. D., Scarfo, N. L., Modi, N. D., Cornelisse, E., Li, L. X., Tan, J. Q. E., Gandhi, J., Maher, D., Kousa, D., Daniel, K., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02494-9 · Cited by 4
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Westley, M., Laffier, J., Rehman, A., Eriksen, G., Sonderby, R., & Sørdahl, M. (2026) · Academia Mental Health and Well-Being · doi:10.20935/mhealthwellb8157 · Cited by 3
Natural language processing in plastic surgery patient consultations
Talwar, A. A., Shen, C., & Shin, J. H. (2025) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.81 · Cited by 4
Habs, M., Knecht, S., & Schmidt‐Wilcke, T. (2025) · Zeitschrift für Evidenz Fortbildung und Qualität im Gesundheitswesen · doi:10.1016/j.zefq.2025.02.007 · Cited by 3
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Omeñaca, A. T., Rocabruna, E. L., Sloan, J., Catta-Preta, M. C., Picó, J. F. I., Alvarez, J. C. A., Solis, T. A., Gil, E. L., Vinaixa, X. S., Villegas, D. V., et al. (2025) · Computers · doi:10.3390/computers14060210 · Cited by 4
Almeida, V. F. A., Dantas, M., & Duncan, A. E. (2025) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000007627 · Cited by 3
Ruan, E., Alkattan, A., Elhadad, N., & Rossetti, S. C. (2024) · medRxiv · doi:10.1101/2024.07.29.24311177 · Cited by 3
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Gasparini, L., Phillipson, N., Daniel Capurro, N., Rosenberg, R., Buttery, J. P., Howley, J., Ranganathan, S., Quinlan, C., Selvadurai, N., Wildenauer, M., et al. (2024) · medRxiv · doi:10.1101/2024.09.11.24313512 · Cited by 3
Shemtob, L., Nouri, A. G., Sullivan, A., Qiu, C. S., Martin, J., Martin, M., Noden, S., Rob, T., Neves, A. L., Majeed, A., et al. (2025) · medRxiv · doi:10.1101/2025.02.21.25322674 · Cited by 3
Osborne, T., Abbasi, S., Hong, S. E., Sexton, R., Ambut, J., Patel, N. J., Rosenthal, R. N., Ung, L., Wang, F., & Wong, R. (2025) · medRxiv · doi:10.1101/2025.04.03.25325204 · Cited by 4
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Liu, X., Garg, M., Jia, H., Sauver, J. S., Pagali, S. R., & Sohn, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-37025-9 · Cited by 2
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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
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Thakkar, J., Kolekar, S., Gite, S., Pradhan, B., & Alamri, A. M. (2024) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2024-0021 · Cited by 2
Groza, T., Rayabsri, W., Gration, D., Hariram, H., Jamuar, S. S., & Baynam, G. S. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.03.014 · Cited by 2
Zając, H. D., & Ternov, N. K. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719979 · Cited by 2
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Benson, R., Kenny, C., Ashraf‐Ganjouei, A., Zhao, M., Darawsheh, R., Qian, A. S., & Hong, J. C. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00112 · Cited by 4
Samardzic, D., Curkovic, J., Okmažić, D., Glumac, S., Vrdoljak, J., Kolega, M. S., & Krešo, A. (2025) · Clinics and Practice · doi:10.3390/clinpract15110208 · Cited by 3
Koppula, M., Madhulika, F., Sreeramoju, N., & Kolimi, P. (2025) · Analytics · doi:10.3390/analytics4040033 · Cited by 2
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Rodriguez, J. A., & Dalal, A. K. (2025) · Medical Clinics of North America · doi:10.1016/j.mcna.2025.11.005 · Cited by 2
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Karmali, Y. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_10 · Cited by 2
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Wong, A., Singh, K., & Nemati, S. (2026) · npj Health Systems · doi:10.1038/s44401-025-00062-z · Cited by 2
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Guo, Y., Guo, Q., Liu, C., Chen, F., Ma, J., Zhang, H., Lv, Y., Yang, T., & Sun, Y. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000004075 · Cited by 2
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Litt, H. K., Wodzinski, A., Subramanian, P., Donovan, M., Vapiwala, N., Narayan, V., Mei, L., Takvorian, S. U., Haas, N. B., Parikh, R. B., et al. (2026) · The Oncologist · doi:10.1093/oncolo/oyag135 · Cited by 2
Zuo, Y., Wan, Q., & Wang, S. (2026) · Journal of Medical Internet Research · doi:10.2196/85770 · Cited by 3
Rust, P., Frings, J., Meister, S., Schulte, F. C., Prinz, C., & Fehring, L. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.100991 · Cited by 5
Enriquez, K. T., Chiang, A. D., Smiley, C., & Staub, M. (2026) · Infection Control and Hospital Epidemiology · doi:10.1017/ice.2026.10480 · Cited by 2
Chang, J., Peng, R., Chen, X., Zhu, Y., Miao, R., Cao, Z., & Feng, H. (2026) · Journal of Medical Internet Research · doi:10.2196/84726 · Cited by 3
Zhang, X., Dai, Y., Zhao, X., Wu, L., Shao, B., Shan, X., Ji, F., Deng, R., & Zhao, B. (2026) · Digital Health · doi:10.1177/20552076261464749 · Cited by 2
Biswas, S., Mondal, R., & Saikia, M. J. (2026) · Future Internet · doi:10.3390/fi18080391 · Cited by 2
Çiftçi, M. U., Koluman, A., Çiftçi, E. A., Çakmur, B. B., & Ziroğlu, N. (2026) · The Knee · doi:10.1016/j.knee.2026.104579 · Cited by 2
Beyond the algorithm to Emotional Communication: Why AI lacks the heart of psychoanalysis
Frankfeldt, V. R. (2025) · Psychoanalysis Self and Context · doi:10.1080/24720038.2025.2483922 · Cited by 2
Li, H., Zeng, C., Miao, L., Wang, Y., Xia, J., & He, D. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003519 · Cited by 2
Performance of Large Language Model-Generated Spanish Discharge Material
Pérez-Guerrero, E. J., Aali, A., Irizarry, E., Corso, N. K., Hom, J., Rodríguez, F., & Santiago, C. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09758-2 · Cited by 2
Takayama, T., Sado, K., Suda, K., Tamura, H., Ueda‐Arakawa, N., Ishihara, K., Ueda, Y., Okamoto, K., Santos, L. H. O., Oshika, T., et al. (2026) · PLOS Digital Health · doi:10.1101/2025.10.06.25337211 · Cited by 3
Hundal, J., Veettil, A. A. V., Chung, C., & Hosseini, M. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-517280 · Cited by 1
Feasibility of Automated Precharting using GPT-4 in New Specialty Referrals.
Liang, A. S., Banda, J. M., Savage, T., Pandya, A., Carey, R., Megwalu, U. C., Chang, M. T., Dash, D., Corbin, C. K., Sharma, A., et al. (2025) · Europe PMC (PubMed Central) · Cited by 1
Wang, C., Liu, Z., Hao, L., Chen, S., Guo, R., Wei, L., Ji, K., Wang, F., & Xu, B. (2025) · Displays · doi:10.1016/j.displa.2025.103144 · Cited by 1
Using large language models to generate child-friendly education materials on myopia
Li, X., Zhang, Y., Zheng, T., Deng, Y., Lu, Y., Hu, J., Chen, S., Li, Y., & Wang, K. (2025) · Digital Health · doi:10.1177/20552076251362338 · Cited by 1
Holley, D., Daly, B. P., Beverly, B., Wamsley, B., Brooks, A., & Zaubler, T. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07088-5 · Cited by 2
Tanzim, U., Khan, I. A., Abikenari, M. A., Al-Deen, R. H., Miah, L., Blaaza, M., Aziz, M. B., Mukadam, Y., & Kerwan, A. (2025) · Premier journal of science. · doi:10.70389/pjs.100095 · Cited by 1
Strachna, O., & Asan, O. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251370743 · Cited by 1
Boddy, N., Reeve, J. L., Spencer, R. A., & Avery, A. J. (2025) · Primary Health Care Research & Development · doi:10.1017/s1463423625100327 · Cited by 1
Pan, J., Tyrtova, E., Wiseman, D. B., Park, C., & Hofstetter, C. P. (2025) · World Neurosurgery · doi:10.1016/j.wneu.2025.124519 · Cited by 1
AI in Geriatric Medicine and Serious Illness Care
Rawal, S., & Shah, A. A. (2025) · Current Geriatrics Reports · doi:10.1007/s13670-025-00445-y · Cited by 1
Clinical Integration of Local AI Assistants Into Emergency Department Discharge Documentation
Silberlust, J., & Genes, N. G. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.38437 · Cited by 1
Guo, Z., Deng, Y., Liang, C., Zhang, H., Li, D., & Huang, M. T. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000004034 · Cited by 1
Human-machine interaction in the age of generative AI
Niraula, D., Shotande, M. O., & El Naqa, I. (2025) · The Cancer Journal · doi:10.1097/ppo.0000000000000797 · Cited by 1
Parker, K. J., Hickman, L., McDonagh, J., Lindley, R. I., & Ferguson, C. (2026) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2026.105521 · Cited by 1
Lingam, V., Lee, J., & Perkins, R. M. (2025) · Medical Clinics of North America · doi:10.1016/j.mcna.2025.07.006 · Cited by 1
Zheng, C., Zhang, Y., Luo, T., Li, F., Qin, Z., Xiong, S., Zhang, J., Lei, E., Lu, L., Zhang, L., et al. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08499-4 · Cited by 3
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Klotzman, V., Kim, A., Walker, B., Leong, S., Ehwerhemuepha, L., & Kelly, R. B. (2026) · Hospitals · doi:10.3390/hospitals3010001 · Cited by 1
Angyal, V., Bertalan, Á., Domján, P., Feith, H. J., & Dinya, E. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1709611 · Cited by 1
Pańczyk, M., Piątek, T. D., Małkowski, P., Hreńczuk, M. K., Jaworski, M., Cieślak, I., & Gotlib, J. (2026) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s566966 · Cited by 1
Angyal, V., Bertalan, Á., Domján, P., Feith, H. J., & Dinya, E. (2026) · Orvosi Hetilap · doi:10.1556/650.2026.33534 · Cited by 1
Generative AI in health sciences education and practice. Part 3: supporting written communication
Reimers, S. J. (2026) · British Journal of Cardiac Nursing · doi:10.12968/bjca.2026.0024 · Cited by 2
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Livieratos, A., Lin, J., Chasani, P., Gaga, M., Fousekis, F. S., Gogos, C. A., Akinosoglou, K., Katsanos, K. H., & Gamalo, M. (2026) · Methods and Protocols · doi:10.3390/mps9030069 · Cited by 1
LLMs as emerging tools for understanding and managing bone metastasis and cancer-induced bone pain
Li, J., Xie, Y., Li, B., He, R., Meng, W., Gong, J., Fan, Y., Li, L., & Li, B. (2026) · iScience · doi:10.1016/j.isci.2026.116158 · Cited by 1
Sethi, I., Khan, S., Lyons, P. G., Gao, C. A., Luo, Y., Miltz, D., Tovar, S., ten Lohuis, C., Polly, D. M., Dave, S. B., et al. (2026) · Critical Care Medicine · doi:10.1097/ccm.0000000000007228 · Cited by 1
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Wang, R., Wang, J., Huang, Y., Guo, H., & Pang, J. (2026) · High-Confidence Computing · doi:10.1016/j.hcc.2026.100415 · Cited by 3
Yang, W. Y., Chen, L., Lan, J., Chen, L., & Li, L. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1895366 · Cited by 1
Heilmeyer, F., Lyssenko, L., Reinhard, T., Haverkamp, C., & Böhringer, D. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001461 · Cited by 1
Gwóźdź-Broczkowska, A. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106645 · Cited by 2
Mobile Health Literacy Interventions and Health Disparities Reduction
Ownby, R. L., Purcell, D. J., Thomas-Purcell, K. B., & Simonson, M. R. (2024) · Springer eBooks · doi:10.1007/978-981-92-1290-3_75 · Cited by 1
Carnino, J. M., Rampam, S., Puyo, E. M., Kennedy, D. G., & Levi, J. R. (2025) · Otolaryngology · doi:10.1002/ohn.70011 · Cited by 1
Generative Artificial Intelligence in Healthcare: Systems Governance, Safety, and Accountability
Silva, M., & Pereira, J. D. (2025) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/o683295214 · Cited by 1
Evaluating the utility of ChatGPT to convert clinic letters into patient friendly languagePreprint
Cork, S. C., & Hopcroft, K. (2024) · medRxiv · doi:10.1101/2024.07.09.24310132 · Cited by 1
Hentel, J. Z., Teichman, K., & Shih, G. (2025) · medRxiv · doi:10.1101/2025.10.10.25337597 · Cited by 1
Harnessing generative AI for quality of life assessment: Foundations for a new research agenda
Zhang, S., Fernandes, S., Boussat, B., Sollari, J., Tran, B. X., Do, H. P., Nguyen, C. T., Auquier, P., & Boyer, L. (2025) · Journal of Epidemiology and Population Health · doi:10.1016/j.jeph.2025.203156 · Cited by 1
From electrocardiogram algorithms to artificial intelligence integration across cardiac pathways
Bowden, T. (2026) · British Journal of Cardiac Nursing · doi:10.12968/bjca.2026.0003 · Cited by 1
Chin, A. T., Zhu, N., Kingsley, T., Mynampati, P., Phipps, Y., Romanov, A., Vangala, S., Weng, M. L., Wisk, L. E., Woo, H. E., et al. (2026) · medRxiv · doi:10.64898/2026.02.20.26346503 · Cited by 1
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
Seethakantha, S. K., Thai, D. N., Gudi, V. P., Tiwari, S., Matar, R., Mitra, A., Zhao, W., McCallum, A., & Salloum, W. (2026) · arXiv · doi:10.48550/arxiv.2605.28910
“Training-time methods include domain pretraining, continual pretraining, supervised fine-tuning, parameter-efficient adapters (Veen et al., 2024; Zaretsky et al., 2024), and specialized loss functions or reinforcement learning objectives (Fabbri et al., 2021; Bao et al., 2025; Asgari et al., 2025).”
Einsatz von Künstlicher Intelligenz in der Notaufnahme
Kümpers, P., Casu, S., & Kumle, B. (2024) · Notaufnahme up2date · doi:10.1055/a-2300-6235
Zhou, H., Wang, H., Duan, Y.-Y., Yan, Z., Luo, R., Lv, X.-X., Xie, Y., Zhang, J., Yang, J.-M., Xue, M., et al. (2024) · Current Medical Science · doi:10.1007/s11596-024-2929-4
Sands, D. Z., & Finn, N. B. (2024) · doi:10.2196/preprints.68911
Deb, B., Fradley, M. G., Cook, S. C., & Barnes, G. D. (2024) · CJC Open · doi:10.1016/j.cjco.2024.11.020
Wang, B., Deng, Y., & Jiang, H. (2024) · doi:10.2196/preprints.69215
Can Electronic care planning using AI Summarization Yield equal Documentation Quality? (EASY eDocQ)Preprint
Dorr, D. A., Weiskopf, N. G., Sabile, J. M. G., Young, E., Mathis, D., Weller, S. L., Alex, J., Doerr, K., & Bedrick, S. D. (2025) · medRxiv · doi:10.1101/2025.01.22.25320982
Metadata
- Authors
- Zaretsky, J., Kim, J. M., Baskharoun, S., Zhao, Y., Austrian, J., Aphinyanaphongs, Y., Gupta, R., Blecker, S. B., & Feldman, J.
- Affiliations
- NYU Langone Health (US)
- NYU Grossman Long Island School of Medicine (US)
- Corresponding author
- Zaretsky, Jonah
- ORCID
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2024-03-11
- Volume
- 7
- Issue
- 3
- Pages
- e240357
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC10928500
- OpenAlex
- W4392643126
- Cited by
- 303
- References
- 36
- Citations by year
- 2026: 131 · 2025: 136 · 2024: 36
- FWCI
- 28.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Linguistics and Language › Text Readability and Simplification
- Keywords
- discharge summaries, large language models, generative AI, readability, comprehensibility, patient education materials, clinical documentation, GPT-4, hallucinations, general internal medicine, patient access to medical records, health information literacy, Flesch-Kincaid Grade Level, PEMAT, natural language processing of clinical notes
- MeSH terms
- Adult, Aged, Artificial Intelligence, Cross-Sectional Studies, Female, Humans, Inpatients, Language, Male, Middle Aged, Patient Discharge, United States, Electronic Health Records
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.
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