{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:42:27Z","timestamp":1775119347936,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Large Language Models (LLMs) are increasingly proposed to personalize healthcare delivery, yet their real-world readiness remains uncertain. We conducted a systematic literature review to assess how LLM-based systems are designed and used to enhance patient engagement and personalization, while identifying open challenges these tools pose. Four digital libraries (Scopus, IEEE Xplore, ACM, and Nature) were searched, yielding 3787 studies; 16 met the inclusion criteria. Most studies, published in 2024, span different types of motivations, architectures, limitations and privacy-preserving approaches. While LLMs show potential in automating patient data collection, recommendation\/therapy generation, and continuous conversational support, their clinical reliability is limited. Most evaluations use synthetic or retrospective data, with only a few employing user studies or scalable simulation environments. This review highlights the tension between innovation and clinical applicability, emphasizing the need for robust evaluation protocols and human-in-the-loop systems to guide the safe and equitable deployment of LLMs in healthcare.<\/jats:p>","DOI":"10.3390\/informatics12040113","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:24:02Z","timestamp":1761056642000},"page":"113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tailoring Treatment in the Age of AI: A Systematic Review of Large Language Models in Personalized Healthcare"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9482-3161","authenticated-orcid":false,"given":"Giordano de Pinho","family":"Souza","sequence":"first","affiliation":[{"name":"Graduate Program in Informatics, Federal University of Rio de Janeiro, Rio de Janeiro, RJ 21941-853, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0092-2171","authenticated-orcid":false,"given":"Glaucia","family":"Melo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B-2K3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2987-4732","authenticated-orcid":false,"given":"Daniel","family":"Schneider","sequence":"additional","affiliation":[{"name":"Systems and Computer Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, RJ 21941-853, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3325\/cmj.2012.53.211","article-title":"Personalized medicine\u2014A tailored health care system: Challenges and opportunities","volume":"53","author":"Louca","year":"2012","journal-title":"Croat. Med. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cinti, C., Trivella, M.G., Joulie, M., Ayoub, H., and Frenzel, M. (2024). The Roadmap toward Personalized Medicine: Challenges and Opportunities. J. Pers. Med., 14.","DOI":"10.3390\/jpm14060546"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/s12967-020-02316-w","article-title":"How personalised medicine will transform healthcare by 2030: The ICPerMed vision","volume":"18","author":"Vicente","year":"2020","journal-title":"J. Transl. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e235","DOI":"10.1016\/S2589-7500(24)00022-0","article-title":"Social Determinants of Health: The Need for Data Science Methods and Capacity","volume":"6","author":"Chunara","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s41386-020-0771-3","article-title":"Opportunities and challenges in the collection and analysis of digital phenotyping data","volume":"46","author":"Onnela","year":"2021","journal-title":"Neuropsychopharmacology"},{"key":"ref_6","unstructured":"OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2024). GPT-4 Technical Report. arXiv."},{"key":"ref_7","unstructured":"Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., and Lundberg, S. (2023). Sparks of Artificial General Intelligence: Early Experiments with GPT-4. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"nwae403","DOI":"10.1093\/nsr\/nwae403","article-title":"A Survey on Multimodal Large Language Models","volume":"11","author":"Yin","year":"2024","journal-title":"Natl. Sci. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fang, C.M., Danry, V., Whitmore, N., Bao, A., Hutchison, A., Pierce, C., and Maes, P. (2024, January 10\u201313). PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models. Proceedings of the 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Houston, TX, USA.","DOI":"10.1109\/BHI62660.2024.10913781"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"So, K., Kim, H.J., Shin, D.S., Sim, J.A., Lee, J.J., Duong, D., Meisinger, K., and Won, D.O. (2025, January 11\u201314). A Conversational Interaction Framework Using Large Language Models for Personalized Elderly Care. Proceedings of the 2025 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE63647.2025.10930020"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kambare, S.M., Jain, K., Kale, I., Kumbhare, V., Lohote, S., and Lonare, S. (2024, January 11\u201313). Design and Evaluation of an AI-Powered Conversational Agent for Personalized Mental Health Support and Intervention (MindBot). Proceedings of the 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India.","DOI":"10.1109\/ICSCNA63714.2024.10863855"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Akilesh, S., Abinaya, R., Dhanushkodi, S., and Sekar, R. (2023, January 1\u20132). A Novel AI-based Chatbot Application for Personalized Medical Diagnosis and Review Using Large Language Models. Proceedings of the 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India.","DOI":"10.1109\/RMKMATE59243.2023.10368616"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pap, I.A., and Oniga, S. (2024). eHealth Assistant AI Chatbot Using a Large Language Model to Provide Personalized Answers through Secure Decentralized Communication. Sensors, 24.","DOI":"10.3390\/s24186140"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Subramanian, S., Han, X., Baldwin, T., Cohn, T., and Frermann, L. (2021). Evaluating debiasing techniques for intersectional biases. arXiv.","DOI":"10.18653\/v1\/2021.emnlp-main.193"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cai, H. (2024, January 13\u201315). Multimodal Hybrid Healthcare Recommendation System Based on ERT-MOE and Large Language Model Enhancement. Proceedings of the 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC), Wuhan, China.","DOI":"10.1109\/EIECC64539.2024.10929467"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s41746-024-01074-z","article-title":"Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI","volume":"7","author":"Abbasian","year":"2024","journal-title":"Npj Digit. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., and Brennan, S.E. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. PLoS Med., 18.","DOI":"10.1371\/journal.pmed.1003583"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","article-title":"Systematic literature reviews in software engineering\u2014A systematic literature review","volume":"51","author":"Kitchenham","year":"2009","journal-title":"Inf. Softw. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infsof.2015.03.007","article-title":"Guidelines for Conducting Systematic Mapping Studies in Software Engineering: An Update","volume":"64","author":"Petersen","year":"2015","journal-title":"Inf. Softw. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kumar, G. (2024, January 21\u201323). A Doctor Assistance Tool: Personalized Healthcare Treatment Recommendations Journey from Deep Reinforcement Learning to Generative AI. Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), New Delhi, India.","DOI":"10.1109\/DELCON64804.2024.10866814"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1038\/s41591-024-03328-5","article-title":"An Evaluation Framework for Clinical Use of Large Language Models in Patient Interaction Tasks","volume":"31","author":"Johri","year":"2025","journal-title":"Nat. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41746-025-01604-3","article-title":"Artificial Intelligence Tools in Supporting Healthcare Professionals for Tailored Patient Care","volume":"8","author":"Kim","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jaiswal, S., Lee, J., Berria, J., Tanikella, R., Zolyomi, A., Ahmad, M.A., and Si, D. (2024, January 22\u201325). Building Personality-Adaptive Conversational AI for Mental Health Therapy. Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Shenzhen, China.","DOI":"10.1145\/3698587.3701489"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8236","DOI":"10.1038\/s41467-024-52415-1","article-title":"Evaluating the Use of Large Language Models to Provide Clinical Recommendations in the Emergency Department","volume":"15","author":"Williams","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Subramanian, A., Yang, Z., Azimi, I., and Rahmani, A.M. (2024, January 15\u201317). Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis. Proceedings of the 2024 IEEE 20th International Conference on Body Sensor Networks (BSN), Chicago, IL, USA.","DOI":"10.1109\/BSN63547.2024.10780466"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Garima, S., Swapnil, M., and Shashank, S. (2024, January 21\u201323). Harnessing the Power of Language Models for Intelligent Digital Health Services. Proceedings of the 2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K), New Delhi, India.","DOI":"10.23919\/ITUK62727.2024.10772761"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., and Al-Hazzaa, S. (2024, January 8\u201312). Next-Generation Virtual Hospital: Integrating Discriminative and Large Multi-Modal Generative AI for Personalized Healthcare. Proceedings of the GLOBECOM 2024\u20142024 IEEE Global Communications Conference, Cape Town, South Africa.","DOI":"10.1109\/GLOBECOM52923.2024.10901624"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Balakrishna, C., Yadav, A., Singh, J., Saba, M., and Shrivastava, V. (2024, January 12\u201314). Smart Drug Delivery Systems Using Large Language Models for Real-Time Treatment Personalization. Proceedings of the 2024 2nd World Conference on Communication & Computing (WCONF), Raipur, India.","DOI":"10.1109\/WCONF61366.2024.10692060"},{"key":"ref_30","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv."},{"key":"ref_31","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., and Iwasawa, Y. (2022). Large Language Models Are Zero-Shot Reasoners. arXiv."},{"key":"ref_32","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language Models Are Few-Shot Learners. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","article-title":"Toward Expert-Level Medical Question Answering with Large Language Models","volume":"31","author":"Singhal","year":"2025","journal-title":"Nat. Med."},{"key":"ref_34","first-page":"1","article-title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","volume":"43","author":"Huang","year":"2025","journal-title":"Acm Trans. Inf. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s44336-024-00009-2","article-title":"A Survey on LLM-based Multi-Agent Systems: Workflow, Infrastructure, and Challenges","volume":"1","author":"Li","year":"2024","journal-title":"Vicinagearth"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ferreira, A.A., Rocha, L., Cunha, W., Machado, A.C., Campos, J.M., Jallais, G., Viana, A.C.F., Tuler, E., Ara\u00fajo, I., and Macul, V. (2025). A comprehensive qualitative analysis of patient dialogue summarization using large language models applied to noisy, informal, non-English real-world data. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-13560-9"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Silva, V., Furtado, E.S., Oliveira, J., and Furtado, V. (2024, January 25\u201328). Engenharia de Prompts em Assistentes Conversacionais para Promo\u00e7\u00e3o de Autocuidado baseados em Modelos Amplos de Linguagem. Proceedings of the Anais do XXIV Simp\u00f3sio Brasileiro de Computa\u00e7\u00e3o Aplicada \u00e0 Sa\u00fade (SBCAS 2024), Goi\u00e2nia, Brazil.","DOI":"10.5753\/sbcas.2024.2252"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.mcpdig.2024.09.006","article-title":"Evaluating Large Language Model\u2013Supported Instructions for Medication Use: First Steps Toward a Comprehensive Model","volume":"2","author":"Reis","year":"2024","journal-title":"Mayo Clin. Proc. Digit. Health"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rajashekar, N.C., Shin, Y.E., Pu, Y., Chung, S., You, K., Giuffre, M., Chan, C.E., Saarinen, T., Hsiao, A., and Sekhon, J. (2024, January 11\u201316). Human-Algorithmic Interaction Using a Large Language Model-Augmented Artificial Intelligence Clinical Decision Support System. Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3613904.3642024"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Park, Y.J., Pillai, A., Deng, J., Guo, E., Gupta, M., Paget, M., and Naugler, C. (2024). Assessing the research landscape and clinical utility of large language models: A scoping review. Bmc Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02459-6"},{"key":"ref_41","unstructured":"Zhang, Z., Rossi, R.A., Kveton, B., Shao, Y., Yang, D., Zamani, H., Dernoncourt, F., Barrow, J., Yu, T., and Kim, S. (2025). Personalization of Large Language Models: A Survey. arXiv."},{"key":"ref_42","unstructured":"Schneider, D., de Almeida, M.A., Nascimento, M., Correia, A., and de Souza, J.M. (2025, January 20\u201321). Designing for (Digital) Nomad-AI Interaction. Proceedings of the International Conference on Computer-Human Interaction Research and Applications, Marbella, Spain."},{"key":"ref_43","unstructured":"Wang, Y., Zhao, Y., and Petzold, L. (2023). Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3359206","article-title":"\u201cHello AI\u201d: Uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making","volume":"3","author":"Cai","year":"2019","journal-title":"Proc. ACM-Hum.-Comput. Interact."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s11280-024-01276-1","article-title":"When large language models meet personalization: Perspectives of challenges and opportunities","volume":"27","author":"Chen","year":"2024","journal-title":"World Wide Web"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/113\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:55:42Z","timestamp":1761058542000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":45,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040113"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040113","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}