هذه الورشة منهج نموذجي بحثي، وليست دورة معتمدة أو بروتوكولاً سريريًا. تأتي النتائج أدناه من دراسات محكَّمة إلا حيث يُشار إلى أنها أدبيات رمادية. بعض النقاط المتعلقة بلغات محددة مستنتجة من النمط العام بأن أداء الذكاء الاصطناعي أسوأ في اللغات الأقل موارد؛ ولا توجد بعد أدلة لكل لغة بالنسبة للعديد من لغات عملائك. عدّل هذه الورشة بما يناسب سياسات مؤسستك قبل الاستخدام.

1. Abrajano, M., Garcia, M., Pope, A., Vidigal, R., Tucker, J. A., & Nagler, J. (2024). How reliance on Spanish-language social media predicts beliefs in false political narratives amongst Latinos. PNAS Nexus, 3(11), pgae442. https://doi.org/10.1093/pnasnexus/pgae442
2. Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–21. https://doi.org/10.1145/3449287
3. Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., & Raffel, C. (2021). Extracting training data from large language models. In 30th USENIX Security Symposium (pp. 2633–2650). USENIX Association. https://arxiv.org/abs/2012.07805
4. Carreras Tartak, J. A., Haimovich, A. D., Thomas, C. E., Bivens Moore, E., Slebonick, A., Haque, N., & Thomas, S. H. (2025). Evaluating the accuracy of Google Translate in emergency department discharge instructions. The Journal of Emergency Medicine, 79, 132–142. https://doi.org/10.1016/j.jemermed.2025.04.024
5. Castellon-Lopez, Y. M., Klomhaus, A. M., Garcia, C., Marquez, D., Avila, H., Gravette, H., Lopez-Chang, R., Ortega, B., Norris, K. C., Brown, A. F., & Blanco, L. (2024). MivacunaLA (MyShotLA): A community-partnered mobile phone intervention to improve COVID-19 vaccination behaviors among low-income, Spanish-speaking, and immigrant Latino parents or caregivers. Vaccines, 12(5), 511. https://doi.org/10.3390/vaccines12050511
6. Cervantes, L., Martin, M., Frank, M. G., Farfan, J. F., Kearns, M., Rubio, L. A., Tong, A., Matus Gonzalez, A., Camacho, C., Collings, A., Mundo, W., Powe, N. R., & Fernandez, A. (2021). Experiences of Latinx individuals hospitalized for COVID-19: A qualitative study. JAMA Network Open, 4(3), e210684. https://doi.org/10.1001/jamanetworkopen.2021.0684
7. Chelli, M., Descamps, J., Lavoue, V., Trojani, C., Azar, M., Deckert, M., Raynier, J.-L., Clowez, G., Boileau, P., & Ruetsch-Chelli, C. (2024). Hallucination rates and reference accuracy of ChatGPT and Bard for systematic reviews: Comparative analysis. Journal of Medical Internet Research, 26, e53164. https://doi.org/10.2196/53164
8. Cronin, A., Kelly, A., Wrona, M., O’Donnell, P., Hassan, A., Myles, T., Fallon, T., & MacFarlane, A. (2025). The patient-safety implications of AI-based communication with migrants in general practice: A scoping review. BJGP Open. https://doi.org/10.3399/BJGPO.2025.0107
9. Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. Journal of Legal Analysis, 16(1), 64–93. https://doi.org/10.1093/jla/laae003
10. Deng, Y., Zhang, W., Pan, S. J., & Bing, L. (2023). Multilingual jailbreak challenges in large language models. arXiv. https://arxiv.org/abs/2310.06474
11. Diamond, L., Izquierdo, K., Canfield, D., Matsoukas, K., & Gany, F. (2019). A systematic review of the impact of patient–physician non-English language concordance on quality of care and outcomes. Journal of General Internal Medicine, 34(8), 1591–1606. https://doi.org/10.1007/s11606-019-04847-5
12. Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022). The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13–29. https://doi.org/10.1038/s44159-021-00006-y
13. Ehrhart, M. G., Aarons, G. A., & Farahnak, L. R. (2014). Assessing the organizational context for EBP implementation: The development and validity testing of the Implementation Climate Scale (ICS). Implementation Science, 9(1), Article 157. https://doi.org/10.1186/s13012-014-0157-1
14. Ettinger de Cuba, S., Miller, D. P., Raifman, J., Cutts, D. B., Bovell-Ammon, A., Frank, D. A., & Jones, D. K. (2023). Reduced health care utilization among young children of immigrants after Donald Trump’s election and proposed public charge rule. Health Affairs Scholar, 1(2), qxad023. https://doi.org/10.1093/haschl/qxad023
15. Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021). Do as AI say: Susceptibility in deployment of clinical decision-aids. npj Digital Medicine, 4(1), Article 31. https://doi.org/10.1038/s41746-021-00385-9
16. Gobezayehu, A. G., Mohammed, H., Dynes, M. M., Desta, B. F., Barry, D., Aklilu, Y., Tessema, H., Tadesse, L., Mikulich, M., Buffington, S. T., & Sibley, L. M. (2014). Knowledge and skills retention among frontline health workers: Community maternal and newborn health training in rural Ethiopia. Journal of Midwifery & Women’s Health, 59(Suppl 1), S21–S31. https://doi.org/10.1111/jmwh.12146
17. Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. https://doi.org/10.1136/amiajnl-2011-000089
18. Gutman, C. K., Cousins, L., Gritton, J., Klein, E. J., Brown, J. C., Scannell, J., & Lion, K. C. (2018). Professional interpreter use and discharge communication in the pediatric emergency department. Academic Pediatrics, 18(8), 935–943. https://doi.org/10.1016/j.acap.2018.07.004
19. Herring, J., & Barnow, B. (2025). Indirect effects of immigration enforcement on health care utilization among lawfully present older Hispanics. Social Science & Medicine, 384, 118540. https://doi.org/10.1016/j.socscimed.2025.118540
20. Jacobs, M., Pradier, M. F., McCoy, T. H., Jr., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021). How machine-learning recommendations influence clinician treatment selections: The example of antidepressant selection. Translational Psychiatry, 11(1), Article 108. https://doi.org/10.1038/s41398-021-01224-x
21. Jin, Y., Chandra, M., Verma, G., Hu, Y., De Choudhury, M., & Kumar, S. (2024). Better to ask in English: Cross-lingual evaluation of large language models for healthcare queries. Proceedings of the ACM Web Conference 2024, 2627–2638. https://doi.org/10.1145/3589334.3645643
22. KFF. (2024–2025). KFF health information and trust tracking polls [Survey series]. Kaiser Family Foundation.
23. Kim, S. S. Y., Liao, Q. V., Vorvoreanu, M., Ballard, S., & Vaughan, J. W. (2024). “I’m not sure, but…”: Examining the impact of large language models’ uncertainty expression on user reliance and trust. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 822–835). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658941
24. Menz, B. D., Modi, N. D., Sorich, M. J., & Hopkins, A. M. (2024). Health disinformation use case highlighting the urgent need for artificial intelligence vigilance: Weapons of mass disinformation. JAMA Internal Medicine, 184(1), 92. https://doi.org/10.1001/jamainternmed.2023.5947
25. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
26. Oh, N., Kim, J., Park, S., An, S., Lee, E., Do, H., Baik, J., Gwon, S. M., Rhu, J., Choi, G. S., Park, S., Cho, J. Y., Lee, H. W., Lee, B., Jeong, E. S., Lee, J. M., Choi, Y., Kwon, J., Kim, K. D., . . . Chun, G. S. (2025). Large language model–assisted surgical consent forms in non-English language: Content analysis and readability evaluation. Journal of Medical Internet Research, 27, e73222. https://doi.org/10.2196/73222
27. Petrov, A., La Malfa, E., Torr, P. H. S., & Bibi, A. (2023). Language model tokenizers introduce unfairness between languages. arXiv. https://arxiv.org/abs/2305.15425
28. Roozenbeek, J., van der Linden, S., Goldberg, B., Rathje, S., & Lewandowsky, S. (2022). Psychological inoculation improves resilience against misinformation on social media. Science Advances, 8(34), eabo6254. https://doi.org/10.1126/sciadv.abo6254
29. Spitale, G., Biller-Andorno, N., & Germani, F. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances, 9(26), eadh1850. https://doi.org/10.1126/sciadv.adh1850
30. Van Veen, D., Van Uden, C., Blankemeier, L., Delbrouck, J.-B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerova, A., Rohatgi, N., Hosamani, P., Collins, W., Ahuja, N., Langlotz, C. P., Hom, J., Gatidis, S., Pauly, J., & Chaudhari, A. S. (2024). Adapted large language models can outperform medical experts in clinical text summarization. Nature Medicine, 30(4), 1134–1142. https://doi.org/10.1038/s41591-024-02855-5
31. Wang, W., Tu, Z., Chen, C., Yuan, Y., Huang, J.-t., Jiao, W., & Lyu, M. R. (2023). All languages matter: On the multilingual safety of large language models. arXiv. https://arxiv.org/abs/2310.00905
32. Wiltsey Stirman, S., Kimberly, J., Cook, N., Calloway, A., Castro, F., & Charns, M. (2012). The sustainability of new programs and innovations: A review of the empirical literature and recommendations for future research. Implementation Science, 7(1), Article 17. https://doi.org/10.1186/1748-5908-7-17
33. Yoo, N., & Jang, S. H. (2026). Who is willing to use AI mental health chatbots? Perceived impact of AI in healthcare predicts acceptance, with higher willingness among immigrants. Computers in Human Behavior Reports, 22, 101111. https://doi.org/10.1016/j.chbr.2026.101111