Trustworthy Cross-Lingual Knowledge Distillation for Clinical NLP: A Conceptual Review of Evidence Preservation, Explainability, Calibration, and Compound Risk
DOI:
https://doi.org/10.64229/b81yxs18Keywords:
Knowledge distillation, Cross-lingual transfer, Clinical NLP, Evidence grounding, Explainable AI, Calibration, TrustworthinessAbstract
Under low-resource conditions, knowledge distillation is a feasible approach to reduce the size of the clinical language models. But similarity between teacher and student task scores is not enough to ensure clinical deployment. The accuracy and F1 may not evolve but the evidence links, contraindications, reasoning boundaries, explanation quality and uncertainty can change during compression. This problem is complicated by the fact that there is limited variation of the terminology, context, population shift, and target language validation as part of the cross lingual transfer. This conceptual review connects the dots on research in clinical NLP, multilingual transfer, compression, factual grounding, explainable AI, calibration, fairness and deployment safety. It is the first to create evidence drift, wherein the link between a recommendation or advice and clinical evidence weakened. Evidence-based distillation, clinicians and patients dual explanations and explanation-based calibration with a taxonomy of 12 categories of single, paired and compounded errors are used for analysis. The loss of evidence and translation error, the incomplete explanation, the overconfidence and the hidden stratification can combine to form a clinical-risk cascade in a chronic disease scenario. Evaluation occurs in the following nodes, Task performance, Evidence fidelity, Explanation fidelity, Calibration, Stakeholder utility and Cascade safety, organized in the format of an operational framework. Their classification and structure assist each other in future empirical studies of the trustworthy multilingual compression. Evaluation should be connected to predictive performance and should be auditable, explainable in a role/aware and culture/aware manner and be calibrated to the target population, include explicit abstention, and include intersectional subgroup monitoring.
References
[1]Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature, 2023, 620, 172-180. DOI: 10.1038/s41586-023-06291-2
[2]Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv preprint arXiv:2303.13375, 2023. DOI: 10.48550/arXiv.2303.13375
[3]OpenAI, Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, et al. GPT-4 technical report. arXiv preprint arXiv:2303.08774, 2023. DOI: 10.48550/arXiv.2303.08774
[4]Minaee S, Mikolov T, Nikzad N, Chenaghlu M, Socher R, Amatriain X, et al. Large language models: A survey. arXiv preprint arXiv:2402.06196, 2024. DOI: 10.48550/arXiv.2402.06196
[5]Yenduri G, Ramalingam M, Chemmalar Selvi GC, Supriya Y, Srivastava G, Maddikunta PKR, et al. GPT (Generative Pre-Trained Transformer)__A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 2024, 12, 54608-54649. DOI: 10.1109/ACCESS.2024.3389497
[6]Jung KH. Large language models in medicine: Clinical applications, technical challenges, and ethical considerations. Healthcare Informatics Research, 2025, 31(2), 114-124. DOI: 10.4258/hir.2025.31.2.114
[7]Yu E, Chu X, Zhang W, Meng X, Yang Y, Ji X, et al. Large language models in medicine: Applications, challenges, and future directions. International Journal of Medical Sciences, 2025, 22(11), 2792-2801. DOI: 10.7150/ijms.111780
[8]Sanh V, Debut L, Chaumond J, Wolf T. DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019. DOI: 10.48550/arXiv.1910.01108
[9]Rohanian O, Nouriborji M, Jauncey H, Kouchaki S, Nooralahzadeh F, Clifton L, et al. Lightweight transformers for clinical natural language processing. Natural Language Engineering, 2024, 30(5), 887-914. DOI: 10.1017/S1351324923000542
[10]Ibrahim ZM, Bean D, Searle T, Qian L, Wu H, Shek A, et al. A knowledge distillation ensemble framework for predicting short- and long-term hospitalization outcomes from electronic health records data. IEEE Journal of Biomedical and Health Informatics, 2022, 26(1), 423-435. DOI: 10.1109/JBHI.2021.3089287
[11]Lee C, Kumar S, Vogt KA, Munshi M, Tallapudi P, Vogt A, et al. Democratizing cost-effective, agentic artificial intelligence to multilingual medical summarization through knowledge distillation. Scientific Reports, 2025, 15(1), 27619. DOI: 10.1038/s41598-025-10451-x
[12]Adams G, Zucker J, Elhadad N. A meta-evaluation of faithfulness metrics for long-form hospital-course summarization. Proceedings of Machine Learning Research, 2023, 219, 2-30.
[13]Zhang N, Zhang Y, Guo W, Mitra P, Zhang R. FaMeSumm: Investigating and improving faithfulness of medical summarization. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2023, 10915-10931. DOI: 10.18653/v1/2023.emnlp-main.673
[14]Li W, Wu W, Chen M, Liu J, Xiao X, Wu H. Faithfulness in natural language generation: A systematic survey of analysis, evaluation and optimization methods. arXiv preprint arXiv:2203.05227, 2022. DOI: 10.48550/arXiv.2203.05227
[15]Trienes J, Joseph S, Schlötterer J, Seifert C, Lo K, Xu W, et al. InfoLossQA: Characterizing and recovering information loss in text simplification. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024, 4263-4294. DOI:10.18653/v1/2024.acl-long.234
[16]Shaitarova A, Zaghir J, Lavelli A, Krauthammer M, Rinaldi F. Exploring the latest highlights in medical natural language processing across multiple languages: A Survey. Yearbook of Medical Informatics, 2023, 32(1), 230-243. DOI: 10.1055/s-0043-1768726
[17]Hershcovich D, Frank S, Lent H, de Lhoneux M, Abdou M, Brandl S, et al. Challenges and strategies in cross-cultural NLP. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2022, 6997-7013. DOI:10.18653/v1/2022.acl-long.482
[18]García Subies G, Barbero Jiménez Á, Martínez Fernández P. A survey of spanish clinical language models. arXiv preprint arXiv:2308.02199, 2023. DOI: 10.48550/arXiv.2308.02199
[19]Yada S, Nakamura Y, Wakamiya S, Aramaki E. Cross-lingual natural language processing on limited annotated case/radiology reports in English and Japanese: Insights from the real-MedNLP workshop. Methods of Information in Medicine, 2024, 63(5-06), 145-163. DOI: 10.1055/a-2405-2489
[20]Asgari E, El Kheir Y, Sadraei Javaheri MA. MorphBPE: A morpho-aware tokenizer bridging linguistic complexity for efficient LLM training across morphologies. arXiv preprint arXiv:2502.00894, 2025. DOI: 10.48550/arXiv.2502.00894
[21]Xu X, Li M, Tao C, Shen T, Cheng R, Li J, et al. A survey on knowledge distillation of large language models. arXiv preprint arXiv:2402.13116, 2024. DOI: 10.48550/arXiv.2402.13116
[22]Wu T, Tao C, Wang J, Yang R, Zhao Z, Wong N. Rethinking Kullback-Leibler divergence in knowledge distillation for large language models. In: Proceedings of the 31st International Conference on Computational Linguistics. Association for Computational Linguistics, 2025, 5737-5755. Available from: https://aclanthology.org/2025.coling-main.383/ (accessed on 4 November 2026)
[23]Chang Y, Wang X, Wang J, Wu Y, Yang L, Zhu K, et al. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 2024, 15(3), 39, 1-45. DOI: 10.1145/3641289
[24]Qin L, Chen Q, Feng X, Wu Y, Zhang Y, Li Y, et al. Large language models meet NLP: A survey. Frontiers of Computer Science, 2026, 20(11), 2011361. DOI: 10.1007/s11704-025-50472-3
[25]Srivastava A, Rastogi A, Rao A, Shoeb AAM, Abid A, Fisch A, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research, 2023. Available from: https://discovery.ucl.ac.uk/id/eprint/10216468/ (accessed on 4 November 2026)
[26]Wang X, Chen N, Chen J, Wang Y, Zhen G, Zhang C, et al. Apollo: A lightweight multilingual medical LLM towards democratizing medical AI to 6B people. arXiv preprint arXiv:2403.03640, 2024. DOI: 10.48550/arXiv.2403.03640
[27]Gangavarapu A. Introducing L2M3, a multilingual medical large language model to advance health equity in low-resource regions. arXiv preprint arXiv:2404.08705, 2024. DOI: 10.48550/arXiv.2404.08705
[28]Labrak Y, Bazoge A, Morin E, Gourraud PA, Rouvier M, Dufour R. BioMistral: A collection of open-source pretrained large language models for medical domains. In: Findings of the Association for Computational Linguistics: ACL 2024. Association for Computational Linguistics, 2024, 5848-5864. DOI: 10.18653/v1/2024.findings-acl.348
[29]Bang Y, Cahyawijaya S, Lee N, Dai W, Su D, Wilie B, et al. A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. In: Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2023, 675-718. DOI: 10.18653/v1/2023.ijcnlp-main.45
[30]Zhang Y, Ives Z, Roth D. Evidence-based trustworthiness. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019, 413-423. DOI: 10.18653/v1/P19-1040
[31]Chandu KR, Bisk Y, Black AW. Grounding grounding in NLP. In: Findings of the Association For Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, 2021, 4283-4305. DOI: 10.18653/v1/2021.findings-acl.375
[32]Kenthapadi K, Sameki M, Taly A. Grounding and evaluation for large language models: Practical challenges and lessons learned (survey). In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2024, 6523-6533. DOI: 10.1145/3637528.3671467
[33]Xie Q, Schenck EJ, Yang HS, Chen Y, Peng Y, Wang F. Faithful AI in medicine: A systematic review with large language models and beyond. medRxiv [Preprint]. 2023, 2023.04.18.23288752. DOI: 10.1101/2023.04.18.23288752
[34]Aljohani M, Hou J, Kommu S, Wang X. A comprehensive survey on the trustworthiness of large language models in healthcare. In: Findings of the Association for Computational Linguistics: EMNLP 2025. Association for Computational Linguistics, 2025, 6720-6748. DOI: 10.18653/v1/2025.findings-emnlp.356
[35]Huang Y, Sun L, Wang H, Wu S, Zhang Q, Li Y, et al. TrustLLLM: Trustworthiness in large language models. In: Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research, 2024, 235, 20166-20270.
[36]He K, Mao R, Lin Q, Ruan Y, Lan X, Feng M, et al. A survey of large language models for healthcare: From data, technology, and applications to accountability and ethics. Information Fusion, 2025, 118, 102963. DOI: 10.1016/j.inffus.2025.102963
[37]Chen Y, Esmaeilzadeh P. Generative AI in medical practice: In-depth exploration of privacy and security challenges. Journal of Medical Internet Research, 2024, 26, e53008. DOI: 10.2196/53008
[38]Alcoba Inciarte A, Kwon SY, Nagoudi EMB, Abdul-Mageed M. On the utility of pretraining language models on synthetic data. In: Proceedings of The Second Arabic Natural Language Processing Conference. Association for Computational Linguistics, 2024, 265-282. DOI: 10.18653/v1/2024.arabicnlp-1.23
[39]Tan Z, Li D, Wang S, Beigi A, Jiang B, Bhattacharjee A, et al. Large language models for data annotation and synthesis: A survey. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024, 930-957. DOI: 10.18653/v1/2024.emnlp-main.54
[40]Papaioannou JM, Grundmann P, van Aken B, Samaras A, Kyparissidis I, Giannakoulas G, et al. Cross-lingual knowledge transfer for clinical phenotyping. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, 2022, 900-909. Available from: https://aclanthology.org/2022.lrec-1.95/ (accessed on 4 November 2026)
[41]Gupta S, Matsubara Y, Chadha A, Moschitti A. Cross-lingual knowledge distillation for answer sentence selection in low-resource languages. In: Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics, 2023, 14078-14092. DOI: 10.18653/v1/2023.findings-acl.885
[42]Nguyen TT, Luu AT. Improving neural cross-lingual abstractive summarization via employing optimal transport distance for knowledge distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(10), 11103-11111. DOI: 10.1609/aaai.v36i10.21359
[43]Basir N, Hakro DN, Khoumbati KUR, Bhatti Z. Leveraging machine-labeled data and cross-lingual transfer for NER in Urdu and Sindhi. Journal of Information & Communication Technology, 2025, 19(1), 1-8.
[44]Stanceski K, Zhong S, Zhang X, Khadra S, Tracy M, Koria L, et al. The quality and safety of using generative AI to produce patient-centred discharge instructions. NPJ Digital Medicine, 2024, 7(1), 329. DOI: 10.1038/s41746-024-01336-w
[45]Tan S, Joty S, Baxter K, Taeihagh A, Bennett GA, Kan MY. Reliability testing for natural language processing systems. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2021, 4153-4169. DOI: 10.18653/v1/2021.acl-long.321
[46]Litschko R, Müller-Eberstein M, van der Goot R, Weber-Genzel L, Plank B. Establishing trustworthiness: Rethinking tasks and model evaluation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2023, 193-203. DOI: 10.18653/v1/2023.emnlp-main.14
[47]Guo Z, Lai A, Thygesen JH, Farrington J, Keen T, Li K. Large language models for mental health applications: Systematic review. JMIR Mental Health, 2024, 11, e57400. DOI: 10.2196/57400
[48]Adams G, Shing HC, Sun Q, Winestock C, McKeown K, Elhadad N. Learning to revise references for faithful summarization. In: Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, 2022, 4009-4027. DOI: 10.18653/v1/2022.findings-emnlp.296
[49]Xie Q, Zhou J, Peng Y, Wang F. FactReranker: Fact-guided reranker for faithful radiology report summarization. arXiv preprint arXiv:2303.08335, 2023. DOI: 10.48550/arXiv.2303.08335
[50]Piya FL, Beheshti R. Agenticsum: An agentic inference-time framework for faithful clinical text summarization. arXiv preprint arXiv:2602.20040, 2026. DOI: 10.48550/arXiv.2602.20040
[51]Zakka C, Shad R, Chaurasia A, Dalal AR, Kim JL, Moor M, et al. Almanac: Retrieval-augmented language models for clinical medicine. NEJM AI, 2024, 1(2), AIoa2300068. DOI: 10.1056/AIoa2300068
[52]Gao Y, Li R, Croxford E, Caskey J, Patterson BW, Churpek M, et al. Leveraging medical knowledge graphs into large language models for diagnosis prediction: Design and application study. JMIR AI, 2025, 4, e58670. DOI: 10.2196/58670
[53]Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI): Toward medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(11), 4793-4813. DOI: 10.1109/TNNLS.2020.3027314
[54]Sadeghi Z, Alizadehsani R, Cifci MA, Kausar S, Rehman R, Mahanta P, et al. A review of explainable artificial intelligence in healthcare. Computers and Electrical Engineering, 2024, 118, 109370. DOI: 10.1016/j.compeleceng.2024.109370
[55]Bharati S, Mondal MRH, Podder P. A review on explainable artificial intelligence for healthcare: Why, how, and when? IEEE Transactions on Artificial Intelligence, 2024, 5(4), 1429-1442, 2024. DOI: 10.1109/TAI.2023.3266418
[56]Mumuni F, Mumuni A. Explainable artificial intelligence (XAI): From inherent explainability to large language models. Array, 2026, 31, 101133. DOI: 10.1016/j.array.2026.101133
[57]Bilal A, Ebert D, Lin B. LLMs for explainable AI: A comprehensive survey. arXiv preprint arXiv:2504.00125, 2025. DOI: 10.48550/arXiv.2504.00125
[58]Donoso-Guzmán I, Kacafírková KS, Szymanski M, Jacobs A, Parra D, Verbert K. A systematic review of user-centred evaluation of explainable AI in healthcare. arXiv preprint arXiv:2506.13904, 2025. DOI: 10.48550/arXiv.2506.13904
[59]Pierce RL, Van Biesen W, Van Cauwenberge D, Decruyenaere J, Sterckx S. Explainability in medicine in an era of AI-based clinical decision support systems. Frontiers in Genetics, 2022, 13, 903600. DOI: 10.3389/fgene.2022.903600
[60]Stiglic G, Kocbek P, Fijacko N, Zitnik M, Verbert K, Cilar L. Interpretability of machine learning-based prediction models in healthcare. WIREs Data Mining and Knowledge Discovery, 2020, 10(5), e1379. DOI: 10.1002/widm.1379
[61]Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing artificial intelligence for clinical decision-making. Frontiers in Digital Health, 2021, 3, 645232. DOI: 10.3389/fdgth.2021.645232
[62]Cabrero-Daniel B, Sanagustín Cabrero A. Perceived trustworthiness of natural language generators. In: Proceedings of the First International Symposium on Trustworthy Autonomous Systems. ACM, 2023, 1-9. DOI: 10.1145/3597512.3599715
[63]Shailya K, Rajpal S, Krishnan GS, Ravindran B. LExT: Towards evaluating trustworthiness of natural language explanations. In: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2025, 1565-1587. DOI: 10.1145/3715275.3732104
[64]Ras G, Xie N, van Gerven M, Doran D. Explainable deep learning: A field guide for the uninitiated. Journal of Artificial Intelligence Research, 2022, 73, 329-397. DOI: 10.1613/jair.1.13200
[65]Roscher R, Bohn B, Duarte MF, Garcke J. Explainable machine learning for scientific insights and discoveries. IEEE Access, 2020, 8, 42200-42216. DOI: 10.1109/ACCESS.2020.2976199
[66]Edin J, Maistro M, Maaløe L, Borgholt L, Havtorn JD, Ruotsalo T. An unsupervised approach to achieve supervised-level explainability in healthcare records. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024, 4869-4890. DOI: 10.18653/v1/2024.emnlp-main.280
[67]Ding K, Zhou M, Chaudhari A, Zhang S, Metaxas DN. Aligning large language models with healthcare stakeholders: A pathway to trustworthy AI integration. arXiv preprint arXiv:2505.02848, 2025. DOI: 10.48550/arXiv.2505.02848
[68]Dubey A, Yang Z, Hattab G. AI readiness in healthcare through storytelling XAI. In: Proceedings of the First Workshop on Explainable Artificial Intelligence for the Medical Domain (EXPLIMED 2024). CEUR Workshop Proceedings, 2024, 3831, paper 12. Available from: https://ceur-ws.org/Vol-3831/ (accessed on 4 November 2026)
[69]Wang C. Calibration in deep learning: A survey of the state-of-the-art. arXiv preprint arXiv:2308.01222, 2023. DOI: 10.48550/arXiv.2308.01222
[70]He W, Jiang Z, Xiao T, Xu Z, Li Y. A survey on uncertainty quantification methods for deep learning. ACM Computing Surveys, 2026, 58(7), 1-35. DOI: 10.1145/3786319
[71]Gao R, Li T, Tang Y, Xu Z, Kammer M, Antic SL, et al. A comparative study of confidence calibration in deep learning: From computer vision to medical imaging. arXiv preprint arXiv:2206.08833, 2022. DOI: 10.48550/arXiv.2206.08833
[72]Omar M, Agbareia R, Glicksberg BS, Nadkarni GN, Klang E. Benchmarking the confidence of large language models in answering clinical questions: Cross-sectional evaluation study. JMIR Medical Informatics, 2025, 13, e66917. DOI: 10.2196/66917
[73]Yu Y, Gomez-Cabello CA, Haider SA, Genovese A, Prabha S, Trabilsy M, et al. Enhancing clinician trust in AI diagnostics: A dynamic framework for confidence calibration and transparency. Diagnostics, 2025, 15(17), 2204. DOI: 10.3390/diagnostics15172204
[74]Popat R, Ive J. Embracing the uncertainty in human-machine collaboration to support clinical decision-making for mental health conditions. Frontiers in Digital Health, 2023, 5, 1188338. DOI: 10.3389/fdgth.2023.1188338
[75]Islam MR. An explainable agentic AI framework for uncertainty-aware and abstention-enabled acute ischemic stroke imaging decisions. arXiv preprint arXiv:2601.01008, 2026. DOI: 10.48550/arXiv.2601.01008
[76]Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 2023, 7(6), 719-742. DOI: 10.1038/s41551-023-01056-8
[77]Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Computing Surveys, 2021, 54(6), 1-35. DOI: 10.1145/3457607
[78]Nazer LH, Zatarah R, Waldrip S, Ke JXC, Moukheiber M, Khanna AK, et al. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2023, 2(6), e0000278. DOI: 10.1371/journal.pdig.0000278
[79]Oakden-Rayner L, Dunnmon J, Carneiro G, Ré C. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: Proceedings of the ACM Conference on Health, Inference, and Learning. ACM, 2020, 2020, 151-159. DOI: 10.1145/3368555.3384468
[80]Le Quy T, Roy A, Iosifidis V, Zhang W, Ntoutsi E. A survey on datasets for fairness-aware machine learning. WIREs Data Mining and Knowledge Discovery, 2022, 12(3), e1452. DOI: 10.1002/widm.1452
[81]Korom R, Kiptinness S, Adan N, Said K, Ithuli C, Rotich O, et al. AI-based clinical decision support for primary care: A real-world study. arXiv preprint arXiv:2507.16947, 2025. DOI: 10.48550/arXiv.2507.16947
[82]Mroz T, Griffin M, Cartabuke R, Laffin L, Russo-Alvarez G, Thomas G, et al. Predicting hypertension control using machine learning. PLoS One, 2024, 19(3), e0299932. DOI: 10.1371/journal.pone.0299932
[83]Araujo-Moura K, Souza L, de Oliveira TA, Rocha MS, De Moraes ACF, Chiavegatto Filho A. Prediction of hypertension in the pediatric population using machine learning and transfer learning: A multicentric analysis of the SAYCARE study. International Journal of Public Health, 2025, 70, 1607944. DOI: 10.3389/ijph.2025.1607944
[84]Bada AB, Garko AB, Gabi D. Hypertension prediction using deep learning with transfer learning techniques. FUDMA Journal of Sciences, 2024, 8(6), 257-263, 2024. DOI: 10.33003/fjs-2024-0806-2855
[85]Liu Y, Mo W, Wang H, Shao Z, Zeng Y, Bi J. Feature selection and risk prediction for diabetic patients with ketoacidosis based on MIMIC-IV. Frontiers in Endocrinology, 2024, 15, 1344277. DOI: 10.3389/fendo.2024.1344277
[86]Zhang W, Yang Z. Association between the triglyceride glucose index and the risk of acute kidney injury in critically ill patients with hypertension: Analysis of the MIMIC-IV database. Frontiers in Endocrinology, 2024, 15, 1437709. DOI: 10.3389/fendo.2024.1437709
[87]SCORE2-Diabetes Working Group and the ESC Cardiovascular Risk Collaboration. SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe. European Heart Journal, 2023, 44(28), 2544-2556. DOI: 10.1093/eurheartj/ehad260
[88]Betzler BK, Chee EYL, He F, Lim CC, Ho J, Hamzah H, et al. Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes. Journal of the American Medical Informatics Association, 2023, 30(12), 1904-1914. DOI: 10.1093/jamia/ocad179
[89]Röhr T, Figueroa A, Papaioannou JM, Fallon C, Bressem K, Nejdl W, et al. Revisiting clinical outcome prediction for MIMIC-IV. In: Proceedings of the 6th Clinical Natural Language Processing Workshop. Association for Computational Linguistics, 2024, 208-217. DOI: 10.18653/v1/2024.clinicalnlp-1.18
[90]Feuerriegel S, Frauen D, Melnychuk V, Schweisthal J, Hess K, Curth A, et al. Causal machine learning for predicting treatment outcomes. Nature Medicine, 2024, 30(4), 958-968. DOI: 10.1038/s41591-024-02902-1
[91]Harrigian K, Tang T, Gonzales A, Cai CX, Dredze M. An eye on Clinical BERT: Investigating language model generalization for diabetic eye disease phenotyping. arXiv preprint arXiv:2311.08687, 2023. DOI: 10.48550/arXiv.2311.08687
[92]Wang F, Preininger A. AI in Health: State of the art, challenges, and future directions. Yearbook of Medical Informatics, 2019, 28(1), 16-26. DOI: 10.1055/s-0039-1677908
[93]Sai S, Gaur A, Sai R, Chamola V, Guizani M, Rodrigues JJPC. Generative AI for transformative healthcare: A comprehensive study of emerging models, applications, case studies, and limitations. IEEE Access, 2024, 12, 31078-31106. DOI: 10.1109/ACCESS.2024.3367715
[94]Peng C, Yang X, Chen A, Smith KE, PourNejatian N, Costa AB, et al. A study of generative large language model for medical research and healthcare. NPJ Digital Medicine, 2023, 6(1), 210. DOI: 10.1038/s41746-023-00958-w
[95]Sharaf S, Anoop VS. An analysis on large language models in healthcare: A case study of BioBERT. arXiv preprint arXiv:2310.07282, 2023. DOI: 10.48550/arXiv.2310.07282
[96]Liévin V, Hother CE, Motzfeldt AG, Winther O. Can large language models reason about medical questions. Patterns, 2024, 5(3), 100943. DOI: 10.1016/j.patter.2024.100943
[97]Thoppilan R, De Freitas D, Hall J, Shazeer N, Kulshreshtha A, Cheng HT, et al. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022. DOI: 10.48550/arXiv.2201.08239
[98]Tong Z, Liu Y, Fan W, Qin J, Han Z, Gong H, et al. Agentic AI in medicine: Architectures, applications, evaluation, and challenges for clinical translation. arXiv preprint arXiv:2607.25489, 2026. DOI: 10.48550/arXiv.2607.25489
[99]Poulett W, Waterhouse A, Wallace B, Kynoch S, Imaz Blanco A, Spence M, et al. A pipeline for generating longitudinal synthetic clinical notes using large language models. arXiv preprint arXiv:2606.26879, 2026. DOI: 10.48550/arXiv.2606.26879
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Copyright (c) 2026 Hamza Qureshi, Hamid Ghous (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
