{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:34:44Z","timestamp":1772112884446,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031962349","type":"print"},{"value":"9783031962356","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-96235-6_12","type":"book-chapter","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T05:03:30Z","timestamp":1750655010000},"page":"154-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Incorporating Medical Assistants in\u00a0eHealth Environments Using an\u00a0Agentic RAG Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8823-9693","authenticated-orcid":false,"given":"Dimitrios","family":"Kalathas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4510-5522","authenticated-orcid":false,"given":"Andreas","family":"Menychtas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-399X","authenticated-orcid":false,"given":"Ilias","family":"Maglogiannis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8503-5784","authenticated-orcid":false,"given":"Panayiotis","family":"Tsanakas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"issue":"7","key":"12_CR1","doi-asserted-by":"publisher","first-page":"371","DOI":"10.3390\/info15070371","volume":"15","author":"HM Alghamdi","year":"2024","unstructured":"Alghamdi, H.M., Mostafa, A.: Towards reliable healthcare LLM agents: a case study for pilgrims during HAJJ. Information 15(7), 371 (2024)","journal-title":"Information"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Angert, T., Suzara, M., Han, J., Pondoc, C., Subramonyam, H.: SpellBurst: a node-based interface for exploratory creative coding with natural language prompts. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pp. 1\u201322 (2023)","DOI":"10.1145\/3586183.3606719"},{"issue":"10377","key":"12_CR3","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/S0140-6736(23)00216-7","volume":"401","author":"A Arora","year":"2023","unstructured":"Arora, A., Arora, A.: The promise of large language models in health care. Lancet 401(10377), 641 (2023)","journal-title":"Lancet"},{"key":"12_CR4","unstructured":"Basit, A., Hussain, K., Hanif, M.A., Shafique, M.: MedAide: leveraging large language models for on-premise medical assistance on edge devices. arXiv preprint arXiv:2403.00830 (2024)"},{"issue":"5","key":"12_CR5","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1038\/s42254-023-00581-4","volume":"5","author":"A Birhane","year":"2023","unstructured":"Birhane, A., Kasirzadeh, A., Leslie, D., Wachter, S.: Science in the age of large language models. Nat. Rev. Phys. 5(5), 277\u2013280 (2023)","journal-title":"Nat. Rev. Phys."},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Chen, C.M., Wang, C.J., Tsai, M.F., Yang, Y.H.: Collaborative similarity embedding for recommender systems. In: The World Wide Web Conference, pp. 2637\u20132643 (2019)","DOI":"10.1145\/3308558.3313493"},{"key":"12_CR7","unstructured":"Choi, J., et al.: MALADE: orchestration of LLM-powered agents with retrieval augmented generation for pharmacovigilance. arXiv preprint arXiv:2408.01869 (2024)"},{"key":"12_CR8","doi-asserted-by":"publisher","first-page":"1166120","DOI":"10.3389\/fpubh.2023.1166120","volume":"11","author":"L De Angelis","year":"2023","unstructured":"De Angelis, L., et al.: ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Front. Public Health 11, 1166120 (2023)","journal-title":"Front. Public Health"},{"key":"12_CR9","unstructured":"Es, S., James, J., Anke, L.E., Schockaert, S.: Ragas: Automated evaluation of retrieval augmented generation. In: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pp. 150\u2013158 (2024)"},{"key":"12_CR10","unstructured":"Han, T., et al.: MedAlpaca\u2013An open-source collection of medical conversational AI models and training data. arXiv preprint arXiv:2304.08247 (2023)"},{"key":"12_CR11","unstructured":"Hershenhouse, J.S., et\u00a0al.: Accuracy, readability, and understandability of large language models for prostate cancer information to the public. In: Prostate Cancer Prostatic Diseases, pp.\u00a01\u20136 (2024)"},{"issue":"7","key":"12_CR12","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s42979-024-03254-w","volume":"5","author":"D Kalathas","year":"2024","unstructured":"Kalathas, D., Koulouris, D., Menychtas, A., Tsanakas, P., Maglogiannis, I.: Continuous machine learning for assisting AR indoor navigation. SN Comput. Sci. 5(7), 913 (2024)","journal-title":"SN Comput. Sci."},{"key":"12_CR13","doi-asserted-by":"publisher","DOI":"10.2196\/59439","volume":"26","author":"Y Ke","year":"2024","unstructured":"Ke, Y., et al.: Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study. J. Med. Internet Res. 26, e59439 (2024)","journal-title":"J. Med. Internet Res."},{"issue":"5","key":"12_CR14","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.jpurol.2023.05.018","volume":"19","author":"JK Kim","year":"2023","unstructured":"Kim, J.K., Chua, M., Rickard, M., Lorenzo, A.: ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic medicine. J. Pediatr. Urol. 19(5), 598\u2013604 (2023)","journal-title":"J. Pediatr. Urol."},{"key":"12_CR15","first-page":"79410","volume":"37","author":"Y Kim","year":"2024","unstructured":"Kim, Y., et al.: MDAgents: an adaptive collaboration of LLMs for medical decision-making. Adv. Neural. Inf. Process. Syst. 37, 79410\u201379452 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"12_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12195-022-00754-8","volume":"16","author":"MR King","year":"2023","unstructured":"King, M.R.: ChatGPT: a conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell. Mol. Bioeng. 16(1), 1\u20132 (2023)","journal-title":"Cell. Mol. Bioeng."},{"key":"12_CR17","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR18","unstructured":"Li, H., Su, Y., Cai, D., Wang, Y., Liu, L.: A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110 (2022)"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Z., Zhang, K., Dan, R., Jiang, S., Zhang, Y.: ChatDoctor: a medical chat model fine-tuned on a large language model meta-AI (llama) using medical domain knowledge. Cureus 15(6), e40895 (2023)","DOI":"10.7759\/cureus.40895"},{"key":"12_CR20","doi-asserted-by":"publisher","DOI":"10.2196\/50638","volume":"25","author":"B Mesk\u00f3","year":"2023","unstructured":"Mesk\u00f3, B.: Prompt engineering as an important emerging skill for medical professionals: tutorial. J. Med. Internet Res. 25, e50638 (2023)","journal-title":"J. Med. Internet Res."},{"issue":"10","key":"12_CR21","doi-asserted-by":"publisher","DOI":"10.2196\/20346","volume":"22","author":"M Milne-Ives","year":"2020","unstructured":"Milne-Ives, M., et al.: The effectiveness of artificial intelligence conversational agents in health care: systematic review. J. Med. Internet Res. 22(10), e20346 (2020)","journal-title":"J. Med. Internet Res."},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Mumtaz, U., Ahmed, A., Mumtaz, S.: LLMs-healthcare: current applications and challenges of large language models in various medical specialties. arXiv preprint arXiv:2311.12882 (2023)","DOI":"10.36922\/aih.2558"},{"key":"12_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/978-3-319-26005-1_22","volume-title":"Ambient Intelligence","author":"C Panagopoulos","year":"2015","unstructured":"Panagopoulos, C., Kalatha, E., Tsanakas, P., Maglogiannis, I.: Evaluation of a mobile home care platform. In: De Ruyter, B., Kameas, A., Chatzimisios, P., Mavrommati, I. (eds.) AmI 2015. LNCS, vol. 9425, pp. 328\u2013343. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-26005-1_22"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Sawarkar, K., Mangal, A., Solanki, S.R.: Blended rag: improving rag (retriever-augmented generation) accuracy with semantic search and hybrid query-based retrievers. In: 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 155\u2013161. IEEE (2024)","DOI":"10.1109\/MIPR62202.2024.00031"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: EHRAgent: code empowers large language models for complex tabular reasoning on electronic health records. arXiv e-prints, pp. arXiv\u20132401 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.1245"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Singh, A., Ehtesham, A., Kumar, S., Khoei, T.T.: Enhancing AI systems with agentic workflows patterns in large language model. In: 2024 IEEE World AI IoT Congress (AIIoT), pp. 527\u2013532. IEEE (2024)","DOI":"10.1109\/AIIoT61789.2024.10578990"},{"key":"12_CR27","unstructured":"Singh, A., Ehtesham, A., Kumar, S., Khoei, T.T.: Agentic retrieval-augmented generation: a survey on agentic rag. arXiv preprint arXiv:2501.09136 (2025)"},{"issue":"1","key":"12_CR28","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41523-023-00557-8","volume":"9","author":"V Sorin","year":"2023","unstructured":"Sorin, V., et al.: Large language model (ChatGPT) as a support tool for breast tumor board. NPJ Breast Cancer 9(1), 44 (2023)","journal-title":"NPJ Breast Cancer"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Suresh, K., Kackar, N., Schleck, L., Fanelli, C.: Towards a rag-based summarization agent for the electron-ion collider. arXiv preprint arXiv:2403.15729 (2024)","DOI":"10.1088\/1748-0221\/19\/07\/C07006"},{"key":"12_CR30","unstructured":"Tan, T.F., et\u00a0al.: Fine-tuning large language model (LLM) artificial intelligence chatbots in ophthalmology and LLM-based evaluation using GPT-4. arXiv preprint arXiv:2402.10083 (2024)"},{"issue":"8","key":"12_CR31","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu, A.J., Ting, D., Elangovan, K., Gutierrez, L., Tan, T.F., Ting, D.: Large language models in medicine. Nat. Med. 29(8), 1930\u20131940 (2023)","journal-title":"Nat. Med."},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Topsakal, O., Akinci, T.C.: Creating large language model applications utilizing LangChain: a primer on developing LLM apps fast. In: International Conference on Applied Engineering and Natural Sciences, vol.\u00a01, pp. 1050\u20131056 (2023)","DOI":"10.59287\/icaens.1127"},{"key":"12_CR33","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1109\/TVCG.2024.3456364","volume":"31","author":"Y Yan","year":"2024","unstructured":"Yan, Y., Hou, Y., Xiao, Y., Zhang, R., Wang, Q.: KNowNet: guided health information seeking from LLMS via knowledge graph integration. IEEE Trans. Vis. Comput. Graph. 31, 547\u2013557 (2024)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"12_CR34","unstructured":"Yu, W., et al.: Generate rather than retrieve: large language models are strong context generators. arXiv preprint arXiv:2209.10063 (2022)"},{"key":"12_CR35","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1002\/med4.70000","volume":"3","author":"H Yuan","year":"2025","unstructured":"Yuan, H.: Agentic large language models for healthcare: current progress and future opportunities. Med. Adv. 3, 37\u201341 (2025)","journal-title":"Med. Adv."},{"key":"12_CR36","unstructured":"Zhao, S., Yang, Y., Wang, Z., He, Z., Qiu, L.K., Qiu, L.: Retrieval augmented generation (rag) and beyond: a comprehensive survey on how to make your LLMS use external data more wisely. arXiv preprint arXiv:2409.14924 (2024)"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96235-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T05:03:35Z","timestamp":1750655015000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96235-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031962349","9783031962356"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96235-6_12","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Limassol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cyprus","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}