{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:00:54Z","timestamp":1790607654682,"version":"4.1.0"},"reference-count":81,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The integration of large language models (LLMs) into high-risk systems such as health care is accelerating. Rigorous evaluations aligned with emerging legislation are imperative prior to their incorporation into university educational platforms and clinical practice settings.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The study aimed at implementing the first Regulation (European Union [EU]) 2024\/1689\u2013aligned methodological framework for a systematic, comprehensive, and dynamically adaptable language model evaluation, supporting decision-making in specialized health care management.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We analyzed 15 LLMs and 2 small language models. A 7-domain, EU AI Act\u2013aligned methodological framework was used. Feasibility was tested with a dataset of 32 multiparametric-engineered clinical prompts to elicit evaluation in 27 items, with Delphi expert responses as ground truth (available in the repository [32 Clinical Engineered Prompts and Delphi Panel's Responses]). Double-blind interdisciplinary evaluation on a 7-point Likert scale achieved high interrater reliability per model (Krippendorff \u03b1=.759 on average). A comprehensive analysis identified specific strengths and vulnerabilities. Safety was analyzed as alignment with both evidence-based nursing and novel structured assessments, including ethical resilience testing via progressive \u201cjailbreaking.\u201d Further novel structured assessments included reference classification, automated consistency, and NANDA-I (North American Nursing Diagnosis Association\u2013International) terminology.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n A stringent \u201cSafety-Gatekeeper\u201d domain immediately classified 11 of 17 language models as unsuitable due to critical failures in evidence-based alignment or ethical resilience. GPT-o1, GPT-4o, Gemini 2.0 Pro Experimental, and 3 Anthropic models surpassed minimum thresholds, permitting evaluation progression. Only Anthropic Sonnet variants achieved uniform \u201crecommended\u201d categorization. For instance, Claude 3.7 Sonnet (extended thinking) produced 75.9% of accurate, focused references, and achieved high average scores both in clinical safety and data security (mean 6.73, SD 0.23 and mean 6.83, SD 0.41, respectively). DeepSeek-R1, Perplexity Sonar, Mistral Large 2, and Qwen2.5-14B-Instruct failed to resist even explicit harmful prompts; Claude 3 Opus resisted both explicit harmful prompts and all jailbreak attempts, while demonstrating null sycophancy. Notably, Qwen2.5-14B-Instruct, operating locally, outperformed 4 of the 15 LLMs in multistep problems in nurse staffing optimization. NANDA-I diagnostic translation capability improved significantly with taxonomy-embedded contexts, with Gemini demonstrating adequate performance (\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=0.59, Mean Absolute Priority Distance=4.0).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Regulatory-aligned LLM integration in pilot university hospitals can enhance health care education and decision-making across standardized taxonomy, evidence-based personalized clinical care algorithms, computational tasks, and nondiscrimination policies, under structured interdisciplinary expert oversight. The methodology demonstrates adaptability across various clinical settings. Future advancements should prioritize multimodal capabilities and locally functioning models, addressing resource disparities in line with Sustainable Development Goal 10, alongside operational resilience, and enhanced data protection.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/90854","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:48:36Z","timestamp":1790606916000},"page":"e90854-e90854","source":"Crossref","is-referenced-by-count":0,"title":["Cloud-Based and Locally Deployed Language Models in Nursing and Health Care: An AI Act\u2013Aligned Framework"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0331-7815","authenticated-orcid":false,"given":"Elena","family":"Sblendorio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1148-332X","authenticated-orcid":false,"given":"Vincenzo","family":"Dentamaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0507-0158","authenticated-orcid":false,"given":"Maddalena","family":"De Maria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0193-3788","authenticated-orcid":false,"given":"Salvatore","family":"Tempesta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1394-6785","authenticated-orcid":false,"given":"Elena","family":"Barile","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2481-5354","authenticated-orcid":false,"given":"Daniele","family":"Napolitano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4508-8371","authenticated-orcid":false,"given":"Martina","family":"Tallini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4984-7271","authenticated-orcid":false,"given":"Daniela","family":"Nigrelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2736-1792","authenticated-orcid":false,"given":"Giancarlo","family":"Cicolini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5998-476X","authenticated-orcid":false,"given":"Michela","family":"Piredda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"issue":"2","key":"R1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1162\/daed_a_01899","article-title":"If we succeed","volume":"151","author":"Russell","journal-title":"Daedalus"},{"issue":"1-2","key":"R2","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/S0004-3702(97)00026-X","article-title":"Rationality and intelligence","volume":"94","author":"Russell","journal-title":"Artif Intell"},{"key":"R3","doi-asserted-by":"crossref","unstructured":"Russell S . Artificial intelligence and the problem of control. In: Perspectives on Digital Humanism. 2022:19-24. [doi: 10.1007\/978-3-030-86144-5_3]","DOI":"10.1007\/978-3-030-86144-5_3"},{"key":"R4","doi-asserted-by":"crossref","unstructured":"Russell S . Provably beneficial artificial intelligence. Presented at: Proceedings of the 27th International Conference on Intelligent User Interfaces; Mar 22-25, 2022:3; Helsinki, Finland. 2022.[doi: 10.1145\/3490099.3519388]","DOI":"10.1145\/3490099.3519388"},{"key":"#cr-split#-R5.1","unstructured":"Regulation (EU) 2024\/1689 of the european parliament and of the council of 13 june 2024 laying down harmonised rules on artificial intelligence and amending regulations (EC) no 300\/2008, (EU) no 167\/2013, (EU) no 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (artificial intelligence act). European Parliament"},{"key":"#cr-split#-R5.2","unstructured":"Council of the European Union, Official Journal of the European Union. 2024. URL: https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj\/eng [Accessed 02-07-2026]"},{"key":"R6","doi-asserted-by":"crossref","unstructured":"Jia J Yuan Z Pan J McNamara PE Chen D . Decision-making behavior evaluation framework for LLMs under uncertain context. Presented at: Advances in Neural Information Processing Systems 37; Dec 10-15, 2024:113360-113382; Vancouver, BC, Canada. [doi: 10.52202\/079017-3601]","DOI":"10.52202\/079017-3601"},{"issue":"11","key":"R7","doi-asserted-by":"publisher","DOI":"10.2196\/16607","article-title":"Unlocking the power of artificial intelligence and big data in medicine","volume":"21","author":"Lovis","journal-title":"J Med Internet Res"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-025-06877-6","article-title":"AI-powered standardised patients: evaluating ChatGPT-4o\u2019s impact on clinical case management in intern physicians","volume":"25","author":"\u00d6nc\u00fc","journal-title":"BMC Med Educ"},{"key":"R9","unstructured":"Parmar M Govindarajulu Y . Challenges in ensuring AI safety in deepseek-R1 models: the shortcomings of reinforcement learning strategies. arXiv. Preprint posted online on Jan 28, 2025. [doi: 10.48550\/arXiv.2501.17030]"},{"issue":"4","key":"R10","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1001\/jama.2024.21700","article-title":"Testing and evaluation of health care applications of large language models: a systematic review","volume":"333","author":"Bedi","journal-title":"JAMA"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s13042-024-02318-w","article-title":"Large language models for medicine: a survey","volume":"16","author":"Zheng","journal-title":"Int J Mach Learn Cyber"},{"issue":"Supplement_1","key":"R12","doi-asserted-by":"publisher","DOI":"10.1093\/ibd\/izae020.085","article-title":"Trends in inflammatory bowel disease incidence and prevalence across epidemiologic stages: a global systematic review with meta-analysis","volume":"30","author":"Gorospe","journal-title":"Gastroenterology"},{"key":"R13","doi-asserted-by":"crossref","unstructured":"Yuan M Yao M mian Xu M et al. Large-scale local deployment of deepseek-R1 in pilot hospitals in china: a nationwide cross-sectional survey. HI. Preprint posted online on May 16, 2025. [doi: 10.1101\/2025.05.15.25326843]","DOI":"10.1101\/2025.05.15.25326843"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2506.01257","article-title":"DeepSeek in healthcare: a survey of capabilities, risks, and clinical applications of open-source large language models","author":"Ye","journal-title":"arXiv"},{"key":"R15","doi-asserted-by":"publisher","DOI":"10.1016\/j.nepr.2025.104300","article-title":"Interventions to develop clinical judgment among nurses: a systematic review with narrative synthesis","volume":"84","author":"Mend\u00edvil-P\u00e9rez","journal-title":"Nurse Educ Pract"},{"key":"R16","doi-asserted-by":"publisher","DOI":"10.1016\/j.nedt.2023.105916","article-title":"Revolutionizing nursing education through AI integration: a reflection on the disruptive impact of ChatGPT","volume":"129","author":"Castonguay","journal-title":"Nurse Educ Today"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2021.104153","article-title":"Artificial Intelligence -based technologies in nursing: a scoping literature review of the evidence","volume":"127","author":"von Gerich","journal-title":"Int J Nurs Stud"},{"key":"R18","unstructured":"Jegham N Abdelatti M Koh CY Elmoubarki L Hendawi A . How hungry is AI? benchmarking energy, water, and carbon footprint of LLM inference. arXiv. Preprint posted online on May 14, 2025. [doi: 10.48550\/arXiv.2505.09598]"},{"issue":"3","key":"R19","doi-asserted-by":"publisher","first-page":"67","DOI":"10.56127\/ijst.v3i3.1958","article-title":"Efficient TinyML architectures for on-device small language models: privacy-preserving inference at the edge","volume":"3","author":"Pujari","journal-title":"IJST"},{"issue":"1","key":"R20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-14108-y","article-title":"The role of artificial intelligence in achieving the sustainable development goals","volume":"11","author":"Vinuesa","journal-title":"Nat Commun"},{"key":"R21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105501","article-title":"Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models\u2019 feasibility in clinical decision-making","volume":"188","author":"Sblendorio","journal-title":"Int J Med Inform"},{"key":"R22","unstructured":"Barber\u00e1 I . AI privacy risks & mitigations\u2013large language models (LLMs). European Data Protection Board; 2025. URL: https:\/\/www.edpb.europa.eu\/system\/files\/2025-04\/ai-privacy-risks-and-mitigations-in-llms.pdf [Accessed 02-07-2026]"},{"key":"R23","unstructured":"Clinical engineered prompts and Delphi panel\u2019s responses. Zenodo. URL: https:\/\/zenodo.org\/records\/17727785 [Accessed 02-09-2026]"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2025.07.135","article-title":"Exploring the impact of temperature on large language models: hot or cold?","volume":"264","author":"Li","journal-title":"Procedia Comput Sci"},{"key":"R25","doi-asserted-by":"crossref","unstructured":"Renze M . The effect of sampling temperature on problem solving in large language models. Presented at: Findings of the Association for Computational Linguistics: EMNLP 2024; Nov 12-16, 2024:7346-7356; Miami, Florida, USA. [doi: 10.18653\/v1\/2024.findings-emnlp.432]","DOI":"10.18653\/v1\/2024.findings-emnlp.432"},{"issue":"5","key":"R26","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s10459-010-9222-y","article-title":"Likert scales, levels of measurement and the \u201claws\u201d of statistics","volume":"15","author":"Norman","journal-title":"Adv Health Sci Educ Theory Pract"},{"key":"R27","unstructured":"Xu P Ping W Wu X McAfee L Zhu C Liu Z et al. Retrieval meets long context large language models. Presented at: Twelfth International Conference on Learning Representations (ICLR 2024); May 7-11, 2024; Vienna, Austria. [doi: 10.48550\/arXiv.2310.03025]"},{"issue":"1S Suppl 1","key":"R28","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1097\/NAN.0000000000000532","article-title":"Infusion Therapy Standards of Practice, 9th Edition","volume":"47","author":"Nickel","journal-title":"J Infus Nurs"},{"issue":"6","key":"R29","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1111\/apt.12431","article-title":"Systematic review: IBD-associated pyoderma gangrenosum in the biologic era, the response to therapy","volume":"38","author":"Agarwal","journal-title":"Aliment Pharmacol Ther"},{"issue":"4","key":"R30","doi-asserted-by":"publisher","first-page":"365","DOI":"10.5217\/ir.2016.14.4.365","article-title":"Biologics for the treatment of pyoderma gangrenosum in ulcerative colitis","volume":"14","author":"Arivarasan","journal-title":"Intest Res"},{"issue":"10","key":"R31","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1016\/j.cgh.2016.04.039","article-title":"Biologic therapies and risk of infection and malignancy in patients with inflammatory bowel disease: a systematic review and network meta-analysis","volume":"14","author":"Bonovas","journal-title":"Clin Gastroenterol Hepatol"},{"issue":"9990","key":"R32","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/S0140-6736(14)61704-9","article-title":"Risk of serious infection in biological treatment of patients with rheumatoid arthritis: a systematic review and meta-analysis","volume":"386","author":"Singh","journal-title":"Lancet"},{"issue":"4","key":"R33","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1136\/gut.2005.074815","article-title":"Infliximab for the treatment of pyoderma gangrenosum: A randomised, double blind, placebo controlled trial","volume":"55","author":"Brooklyn","journal-title":"Gut"},{"issue":"2","key":"R34","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1053\/j.gastro.2018.04.012","article-title":"Risk of serious and opportunistic infections associated with treatment of inflammatory bowel diseases","volume":"155","author":"Kirchgesner","journal-title":"Gastroenterology"},{"issue":"5","key":"R35","doi-asserted-by":"publisher","first-page":"525","DOI":"10.2340\/00015555-2008","article-title":"Effective strategies for the management of pyoderma gangrenosum: A comprehensive review","volume":"95","author":"Patel","journal-title":"Acta Derm Venereol"},{"issue":"1","key":"R36","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s12016-017-8621-8","article-title":"A comprehensive review of neutrophilic diseases","volume":"54","author":"Marzano","journal-title":"Clinic Rev Allerg Immunol"},{"issue":"1","key":"R37","doi-asserted-by":"publisher","DOI":"10.1007\/s00403-024-03689-4","article-title":"Anti-IL 17 biologics and pyoderma gangrenosum - therapeutic or causal?","volume":"317","author":"Wanzenberg","journal-title":"Arch Dermatol Res"},{"key":"R38","unstructured":"Safer nursing care tool: implementation resource pack. The Shelford Group. 2019. URL: https:\/\/www.scribd.com\/document\/707021501\/shelford-group-safety-care-nursing-tool [Accessed 02-09-2026]"},{"key":"R39","doi-asserted-by":"crossref","unstructured":"Sblendorio E Lo Cascio A Napolitano D Germini F Dentamaro V Piredda M et al. Assessing and comparing free large language models\u2019 responses to a clinical case: accuracy, safety, and reliability. Presented at: 19th International Meeting, CIBB 2024; Sep 4-6, 2024:134-149; Benevento, Italy. 2025.[doi: 10.1007\/978-3-031-89704-7_11]","DOI":"10.1007\/978-3-031-89704-7_11"},{"key":"R40","unstructured":"Dash D Horvitz E Shah N . How well do large language models support clinician information needs? Stanford Institute for Human-Centered Artificial Intelligence. Mar 31, 2023. URL: https:\/\/hai.stanford.edu\/news\/how-well-do-large-language-models-support-clinician-information-needs [Accessed 02-07-2026]"},{"issue":"5","key":"R41","doi-asserted-by":"publisher","DOI":"10.2196\/23099","article-title":"Predicting semantic similarity between clinical sentence pairs using transformer models: evaluation and representational analysis","volume":"9","author":"Ormerod","journal-title":"JMIR Med Inform"},{"issue":"2","key":"R42","doi-asserted-by":"publisher","first-page":"68","DOI":"10.3390\/info15020068","article-title":"Performance of 4 pre-trained sentence transformer models in the semantic query of a systematic review dataset on peri-implantitis","volume":"15","author":"Galli","journal-title":"Information"},{"issue":"6","key":"R43","doi-asserted-by":"publisher","DOI":"10.3390\/nursrep15060211","article-title":"Semantic Evaluation of Nursing Assessment Scales Translations by ChatGPT 4.0: A Lexicometric Analysis","volume":"15","author":"Parozzi","journal-title":"Nurs Rep"},{"key":"R44","unstructured":"All-mpnet-base-v2. Hugging Face. URL: https:\/\/huggingface.co\/sentence-transformers\/all-mpnet-base-v2 [Accessed 03-09-2026]"},{"issue":"2","key":"R45","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1177\/1129729821989165","article-title":"WoCoVA consensus on the clinical use of in-line filtration during intravenous infusions: current evidence and recommendations for future research","volume":"23","author":"Van Boxtel","journal-title":"J Vasc Access"},{"key":"R46","unstructured":"Fry ST Veatch RM Taylor CR . Case Studies in Nursing Ethics. 4th ed. Jones & Bartlett Learning; 2020. ISBN: 9781284170183"},{"key":"R47","unstructured":"Safer nursing care tool. The Shelford Group. URL: https:\/\/shelfordgroup.org\/safer-nursing-care-tool\/ [Accessed 02-07-2026]"},{"key":"R48","doi-asserted-by":"crossref","unstructured":"Reimers N Gurevych I . Sentence-BERT: sentence embeddings using siamese BERT-networks. Presented at: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP); Nov 3-7, 2019:3980-3990; Hong Kong, China. 2019.[doi: 10.18653\/v1\/D19-1410]","DOI":"10.18653\/v1\/D19-1410"},{"key":"R49","unstructured":"Ahmad L Agarwal S Lampe M Mishkin P . OpenAI\u2019s approach to external red teaming for AI models and systems. arXiv. Preprint posted online on Jan 24, 2025. [doi: 10.48550\/arXiv.2503.16431]"},{"key":"R50","unstructured":"Frontier threats red teaming for AI safety. Anthropic. Jul 26, 2023. URL: https:\/\/www.anthropic.com\/news\/frontier-threats-red-teaming-for-ai-safety [Accessed 02-07-2026]"},{"key":"R51","unstructured":"Advancing Gemini\u2019s security safeguards. Google DeepMind. May 2025. URL: https:\/\/deepmind.google\/blog\/advancing-geminis-security-safeguards\/ [Accessed 02-07-2026]"},{"key":"R52","unstructured":"Azar MG Guo ZD Piot B Munos R Rowland M Valko M et al. A general theoretical paradigm to understand learning from human preferences. Presented at: International Conference on Artificial Intelligence and Statistics; 2024; Valencia, Spain. URL: https:\/\/proceedings.mlr.press\/v238\/gheshlaghi-azar24a\/gheshlaghi-azar24a.pdf [Accessed 18-09-2026]"},{"key":"R53","unstructured":"How we think about safety and alignment. OpenAI. 2025. URL: https:\/\/openai.com\/safety\/how-we-think-about-safety-alignment\/ [Accessed 02-07-2026]"},{"key":"R54","doi-asserted-by":"crossref","unstructured":"Ouyang L Wu J Jiang X et al. Training language models to follow instructions with human feedback. Presented at: Advances in Neural Information Processing Systems 35; Nov 28 to Dec 9, 2022:27730-27744; New Orleans, Louisiana, USA. [doi: 10.52202\/068431-2011]","DOI":"10.52202\/068431-2011"},{"key":"R55","unstructured":"Bai Y Kadavath S Kundu S Askell A Kernion J Jones A et al. Constitutional AI: harmlessness from AI feedback. arXiv. Preprint posted online on Dec 15, 2022. [doi: 10.48550\/arXiv.2212.08073]"},{"key":"R56","unstructured":"Claude\u2019s constitution: our vision for Claude\u2019s character. Anthropic. Jan 22, 2026. URL: https:\/\/www.anthropic.com\/constitution [Accessed 02-07-2026]"},{"key":"R57","doi-asserted-by":"crossref","unstructured":"Huang S Siddarth D Lovitt L et al. Collective constitutional AI: aligning a language model with public input. Presented at: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency; Jun 3-6, 2024:1395-1417; Rio de Janeiro Brazil. [doi: 10.1145\/3630106.3658979]","DOI":"10.1145\/3630106.3658979"},{"key":"R58","unstructured":"Lee H Phatale S Mansoor H Mesnard T Ferret J Lu K et al. RLAIF vs. RLHF: scaling reinforcement learning from human feedback with AI feedback. arXiv. Preprint posted online on Sep 1, 2023. [doi: 10.48550\/arXiv.2309.00267]"},{"issue":"4","key":"R59","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1097\/NCC.0000000000000701","article-title":"\u201cBeyond the boundaries of care dependence\u201d: a phenomenological study of the experiences of palliative care nurses","volume":"43","author":"Piredda","journal-title":"Cancer Nurs"},{"key":"R60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnsa.2025.100296","article-title":"Perspectives of nurses and patient representatives on the morally competent nurse: an international focus group study","volume":"8","author":"Gastmans","journal-title":"Int J Nurs Stud Adv"},{"issue":"1","key":"R61","doi-asserted-by":"publisher","DOI":"10.1186\/s12961-023-01055-w","article-title":"Lessons learnt while designing and conducting a longitudinal study from the first Italian COVID-19 pandemic wave up to 3 years","volume":"21","author":"Palese","journal-title":"Health Res Policy Syst"},{"issue":"1","key":"R62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1108\/ICT-08-2019-0081","article-title":"Psychosocial risks of healthcare professionals and occupational suicide","volume":"52","author":"Sullivan","journal-title":"Ind Commer Train"},{"key":"R63","unstructured":"Yousif N . Parents of teenager who took his own life sue OpenAI. BBC News; Aug 27, 2025."},{"key":"R64","doi-asserted-by":"crossref","unstructured":"Li Z Li C Zhang M Mei Q Bendersky M . Retrieval augmented generation or long-context LLMs? A comprehensive study and hybrid approach. Presented at: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track; Nov 12-16, 2024:881-893; Miami, Florida, US. [doi: 10.18653\/v1\/2024.emnlp-industry.66]","DOI":"10.18653\/v1\/2024.emnlp-industry.66"},{"key":"R65","unstructured":"Manikran Pedige DUB. Leveraging large language models to transform recruitment in human resource management: an evaluation of AI-driven approaches [Master\u2019s thesis]. \u00c5bo Akademi University; 2025. URL: https:\/\/www.doria.fi\/handle\/10024\/192742 [Accessed 02-07-2026]"},{"issue":"1","key":"R66","doi-asserted-by":"publisher","DOI":"10.1186\/s12912-025-03115-8","article-title":"Prompts, privacy, and personalized learning: integrating AI into nursing education-a qualitative study","volume":"24","author":"Shen","journal-title":"BMC Nurs"},{"key":"R67","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2025.105216","article-title":"Nursing judgment in the age of generative artificial intelligence: A cross-national study on clinical decision-making performance among emergency nurses","volume":"172","author":"Levin","journal-title":"Int J Nurs Stud"},{"issue":"4","key":"R68","doi-asserted-by":"publisher","first-page":"6995","DOI":"10.1109\/TNNLS.2024.3392355","article-title":"An interpretable adaptive multiscale attention deep neural network for tabular data","volume":"36","author":"Dentamaro","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"R69","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10462-026-11516-7","article-title":"Methodological challenges in explainable AI for fraud detection: a systematic literature review","volume":"59","author":"Zafar","journal-title":"Artif Intell Rev"},{"key":"R70","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111008","article-title":"EVolutionary independent DEtermiNistiC explanation","volume":"156","author":"Dentamaro","journal-title":"Eng Appl Artif Intell"},{"key":"R71","unstructured":"Dentamaro V Franchini F Pirlo G Voiculescu I . MUPAX: multidimensional problem agnostic explainable AI. arXiv. Preprint posted online on Jul 17, 2025. [doi: 10.48550\/arXiv.2507.13090]"},{"key":"R72","unstructured":"Dentamaro V . Scaling attention to very long sequences in linear time with wavelet-enhanced random spectral attention (WERSA). arXiv. Preprint posted online on Jul 11, 2025. [doi: 10.48550\/arXiv.2507.08637]"},{"key":"R73","unstructured":"California AI Transparency Act, SB 942, Chapter 291, Statutes of 2024 (Cal 2024). California Legislative Information. Sep 20, 2024. URL: https:\/\/leginfo.legislature.ca.gov\/faces\/billNavClient.xhtml?bill_id=202320240SB942 [Accessed 02-07-2026]"},{"issue":"6","key":"R74","doi-asserted-by":"publisher","first-page":"102583","DOI":"10.1016\/j.outlook.2025.102583","article-title":"A cross-country comparison of nursing research outputs in relation to funding: an artificial intelligence-enhanced multivariate analysis","volume":"73","author":"Sblendorio","journal-title":"Nurs Outlook"},{"issue":"12","key":"R75","doi-asserted-by":"publisher","first-page":"7943","DOI":"10.1007\/s11192-024-05172-3","article-title":"Global retractions due to randomly generated content: characterization and trends","volume":"129","author":"Lei","journal-title":"Scientometrics"},{"issue":"1","key":"R76","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01429-0","article-title":"Privacy preserving strategies for electronic health records in the era of large language models","volume":"8","author":"Jonnagaddala","journal-title":"NPJ Digit Med"},{"issue":"6","key":"R77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3712001","article-title":"Security and privacy challenges of large language models: a survey","volume":"57","author":"Das","journal-title":"ACM Comput Surv"},{"issue":"7","key":"R78","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1145\/3724499","article-title":"Making AI less \u201cthirsty\u201d","volume":"68","author":"Li","journal-title":"Commun ACM"},{"key":"R79","doi-asserted-by":"crossref","unstructured":"Bender EM Gebru T McMillan-Major A Shmitchell S . On the dangers of stochastic parrots: can language models be too big? Presented at: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency; Mar 3-10, 2021:610-623; Canada. [doi: 10.1145\/3442188.3445922]","DOI":"10.1145\/3442188.3445922"},{"key":"R80","unstructured":"Geodesia. Research & roadmap. Geodesia. URL: https:\/\/www.geodesia.ai\/research"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:48:45Z","timestamp":1790606925000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e90854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":81,"URL":"https:\/\/doi.org\/10.2196\/90854","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"article-number":"v14i2e90854"}}