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WANG Zixing, QI Le, LIAN Xiaodan, ZHOU Ziheng, MENG Aiwei, WU Xintong, GAO Xiaoyuan, YANG Yujie, LIU Yiyang, ZHAO Wei, DIAO Xiaolin. The Development and Application of Chatbots in Healthcare: From Traditional Methods to Large Language Models[J]. Medical Journal of Peking Union Medical College Hospital. DOI: 10.12290/xhyxzz.2024-0824
Citation: WANG Zixing, QI Le, LIAN Xiaodan, ZHOU Ziheng, MENG Aiwei, WU Xintong, GAO Xiaoyuan, YANG Yujie, LIU Yiyang, ZHAO Wei, DIAO Xiaolin. The Development and Application of Chatbots in Healthcare: From Traditional Methods to Large Language Models[J]. Medical Journal of Peking Union Medical College Hospital. DOI: 10.12290/xhyxzz.2024-0824
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The Development and Application of Chatbots in Healthcare: From Traditional Methods to Large Language Models

  • 1 Graduate School, Chinese Academy of Medical Sciences&Peking Union Medical College, Beijing 100730, China;

  • 2 Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences&Peking Union Medical College, Beijing 100037, China

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Application of Artificial Intelligence and Information Technology Research Fund of Fuwai Hospital (2024-AI35)

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  • Received Date: October 22, 2024
  • Accepted Date: December 22, 2024
  • Available Online: January 15, 2025
    Graphical Abstract
    • Abstract
  • With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of healthcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots ,aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.
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  • [1]
    Wang H, Wang L, Du Y, et al. A survey of the evolution of language model-based dialogue systems[J]. arXiv preprint arXiv:2311.16789, 2023.
    [2]
    Wu T, He S, Liu J, et al. A brief overview of ChatGPT: The history, status quo and potential future development[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(5): 1122-1136.
    [3]
    Thirunavukarasu A J, Ting D S J, Elangovan K, et al. Large language models in medicine[J]. Nature medicine, 2023, 29(8): 1930-1940.
    [4]
    Bao Z, Chen W, Xiao S, et al. Disc-medllm: Bridging general large language models and real-world medical consultation[J]. arxiv preprint arxiv:2308.14346, 2023.
    [5]
    Zhu Y, Feng S, Wang D, et al. Knowledge-Enhanced Interactive Matching Network for Multi-turn Response Selection in Medical Dialogue Systems[C]//International Conference on Database Systems for Advanced Applications. Cham: Springer International Publishing, 2022: 255-262.
    [6]
    Weld H, Huang X, Long S, et al. A survey of joint intent detection and slot filling models in natural language understanding[J]. ACM Computing Surveys, 2022, 55(8): 1-38.
    [7]
    Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
    [8]
    Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer[J]. Journal of machine learning research, 2020, 21(140): 1-67.
    [9]
    Barile J, Milanaik R. Further Reflections on the Use of Large Language Models in Pediatrics—Reply[J]. JAMA pediatrics, 2024.
    [10]
    Yang S, Zhao H, Zhu S, et al. Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(17): 19368-19376.
    [11]
    Shi X, Liu Z, Du L, et al. Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges[J]. arXiv preprint arXiv:2405.10630, 2024.
    [12]
    Grassini E, Buzzi M, Leporini B, et al. A systematic review of chatbots in inclusive healthcare: insights from the last 5 years[J]. Universal Access in the Information Society, 2024: 1-9.
    [13]
    Liao K, ZHONG C, Chen W, et al. Task-oriented dialogue system for automatic disease diagnosis via hierarchical reinforcement learning[J]. 2020.
    [14]
    Zhao X, Chen L, Chen H. A weighted heterogeneous graph-based dialog system[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 34(8): 5212-5217.
    [15]
    Savage T, Nayak A, Gallo R, et al. Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine[J]. NPJ Digital Medicine, 2024, 7(1): 20.
    [16]
    Dou C, Jin Z, Jiao W, et al. Plugmed: Improving specificity in patient-centered medical dialogue generation using in-context learning[J]. arxiv preprint arxiv:2305.11508, 2023.
    [17]
    Zhang H, Li J, Wang Y, et al. Integrating automated knowledge extraction with large language models for explainable medical decision-making[C]//2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2023: 1710-1717.
    [18]
    Wang H, Zhao S, Qiang Z, et al. Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis[J]. arxiv preprint arxiv:2401.16107, 2024.
    [19]
    Li J, Wang S, Zhang M, et al. Agent hospital: A simulacrum of hospital with evolvable medical agents[J]. arxiv preprint arxiv:2405.02957, 2024.
    [20]
    Wang C, Liu S, Li A, et al. Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study[J]. Journal of Medical Internet Research, 2023, 25: e51501.
    [21]
    Yang J, Shu L, Han M, et al. RDmaster: A novel phenotype-oriented dialogue system supporting differential diagnosis of rare disease[J]. Computers in Biology and Medicine, 2024, 169: 107924.
    [22]
    Song M, Wang J, Yu Z, et al. PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis[J]. Medical Image Analysis, 2024: 103248.
    [23]
    Tang F, Uchendu I, Wang F, et al. Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent[J]. Scientific reports, 2020, 10(1): 5732.
    [24]
    Zhang S, Song J. A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model[J]. Scientific Reports, 2024, 14(1): 17118.
    [25]
    Shi X, Liu Z, Wang C, et al. Midmed: Towards mixed-type dialogues for medical consultation[J]. arxiv preprint arxiv:2306.02923, 2023.
    [26]
    Athota L, Shukla V K, Pandey N, et al. Chatbot for healthcare system using artificial intelligence[C]//20208th International conference on reliability, infocom technologies and optimization (trends and future directions)(ICRITO). IEEE, 2020: 619-622.
    [27]
    Zhu M, Ahuja A, Wei W, et al. A hierarchical attention retrieval model for healthcare question answering[C]//The World Wide Web Conference. 2019: 2472-2482.
    [28]
    Qiu Y, Li M, Wang Y, et al. Hierarchical type constrained topic entity detection for knowledge base question answering[C]//Companion Proceedings of the The Web Conference 2018. 2018: 35-36.
    [29]
    Sima A C, Mendes de Farias T, Anisimova M, et al. Bio-SODA UX: enabling natural language question answering over knowledge graphs with user disambiguation[J]. Distributed and Parallel Databases, 2022, 40(2): 409-440.
    [30]
    Huang L, Yu W, Ma W, et al. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions[J]. ACM Transactions on Information Systems, 2023.
    [31]
    Ke Y H, Jin L, Elangovan K, et al. Development and Testing of Retrieval Augmented Generation in Large Language Models--A Case Study Report[J]. arXiv preprint arXiv:2402.01733, 2024.
    [32]
    Chen S, Wu M, Zhu K Q, et al. LLM-empowered chatbots for psychiatrist and patient simulation: application and evaluation[J]. arxiv preprint arxiv:2305.13614, 2023.
    [33]
    Oguntimilehin A, Babalola G O, Akinduyite C O, et al. A Machine Learning Driven Malaria Fever Medical Consultation Chatbot[C]//2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024: 1-6.
    [34]
    Kuşcu O, Pamuk A E, Sütay Süslü N, et al. Is ChatGPT accurate and reliable in answering questions regarding head and neck cancer?[J]. Frontiers in Oncology, 2023, 13: 1256459.
    [35]
    Zand A, Sharma A, Stokes Z, et al. An exploration into the use of a chatbot for patients with inflammatory bowel diseases: retrospective cohort study[J]. Journal of medical Internet research, 2020, 22(5): e15589.
    [36]
    Miner A S, Laranjo L, Kocaballi A B. Chatbots in the fight against the COVID-19 pandemic[J]. NPJ digital medicine, 2020, 3(1): 65.
    [37]
    World Health Organization. Chatbots against COVID-19: Using Chatbots to Answer Questions on COVID-19 in the User’s Language[J]. 2020.
    [38]
    Zhou H, Huang M, Zhang T, et al. Emotional chatting machine: Emotional conversation generation with internal and external memory[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1).
    [39]
    Li Y, Li K, Ning H, et al. Towards an online empathetic chatbot with emotion causes[C]//Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021: 2041-2045.
    [40]
    Tu Q, Li Y, Cui J, et al. Misc: a mixed strategy-aware model integrating comet for emotional support conversation[J]. arxiv preprint arxiv:2203.13560, 2022.
    [41]
    Ghosh S, Chollet M, Laksana E, et al. Affect-lm: A neural language model for customizable affective text generation[J]. arxiv preprint arxiv:1704.06851, 2017.
    [42]
    Izumi K, Tanaka H, Shidara K, et al. Response Generation for Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioning[J]. arxiv preprint arxiv:2401.15966, 2024.
    [43]
    Nie J, Shao H, Fan Y, et al. LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devices[J]. arXiv preprint arXiv:2403.10779, 2024.
    [44]
    Zhang L, Jin R. Attentive goal-based dialogue system for ADHD behavior treatment[C]//Proceedings of the 20204th International Conference on Computer Science and Artificial Intelligence. 2020: 1-7.
    [45]
    Trappey A J C, Lin A P C, Hsu K Y K, et al. Development of an empathy-centric counseling chatbot system capable of sentimental dialogue analysis[J]. Processes, 2022, 10(5): 930.
    [46]
    Qiu J, Lam K, Li G, et al. LLM-based agentic systems in medicine and healthcare[J]. Nature Machine Intelligence, 2024: 1-3.
    [47]
    Lee J, Kim H, Kim K H, et al. Effective virtual patient simulators for medical communication training: a systematic review[J]. Medical education, 2020, 54(9): 786-795.
    [48]
    Campillos-Llanos L, Thomas C, Bilinski É, et al. Designing a virtual patient dialogue system based on terminology-rich resources: Challenges and evaluation[J]. Natural Language Engineering, 2020, 26(2): 183-220.
    [49]
    Dolianiti F, Tsoupouroglou I, Antoniou P, et al. Chatbots in healthcare curricula: the case of a conversational virtual patient[C]//Brain Function Assessment in Learning: Second International Conference, BFAL 2020, Heraklion, Crete, Greece, October 9–11, 2020, Proceedings 2. Springer International Publishing, 2020: 137-147.
    [50]
    Cook D A. Creating virtual patients using large language models: scalable, global, and low cost[J]. Medical teacher, 2024: 1-3.
    [51]
    Grévisse C. RasPatient Pi: A Low-Cost Customizable LLM-Based Virtual Standardized Patient Simulator[C]//International Conference on Applied Informatics. Cham: Springer Nature Switzerland, 2024: 125-137.
    [52]
    Li Y, Zeng C, Zhong J, et al. Leveraging large language model as simulated patients for clinical education[J]. arXiv preprint arXiv:2404.13066, 2024.
    [53]
    Maharjan R, Bækgaard P, Bardram J E. " Hear me out" smart speaker based conversational agent to monitor symptoms in mental health[C]//Adjunct proceedings of the 2019 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2019 ACM international symposium on wearable computers. 2019: 929-933.
    [54]
    Lee J Y, Kim J Y, You S J, et al. Development and usability of a life-logging behavior monitoring application for obese patients[J]. Journal of obesity & metabolic syndrome, 2019, 28(3): 194.
    [55]
    Kocabiyikoglu A C, Portet F, Babouchkine J M, et al. Spoken Dialogue System for Medical Prescription Acquisition on Smartphone: Development, Corpus and Evaluation[J]. arxiv preprint arxiv:2311.03510, 2023.
    [56]
    Xu K, Hou W, Cheng Y, et al. Medical dialogue generation via dual flow modeling[J]. arxiv preprint arxiv:2305.18109, 2023.
    [57]
    Tayal A, Di Eugenio B, Salunke D, et al. A Neuro-Symbolic Approach to Monitoring Salt Content in Food[J]. arxiv preprint arxiv:2404.01182, 2024.
    [58]
    Qiu H, Li A, Ma L, et al. PsyChat: A Client-Centric Dialogue System for Mental Health Support[J]. arxiv preprint arxiv:2312.04262, 2023.
    [59]
    Adikari A, De Silva D, Moraliyage H, et al. Empathic conversational agents for real-time monitoring and co-facilitation of patient-centered healthcare[J]. Future Generation Computer Systems, 2022, 126: 318-329.
    [60]
    Compton R, Valmianski I, Deng L, et al. Medcod: A medically-accurate, emotive, diverse, and controllable dialog system[C]//Machine Learning for Health. PMLR, 2021: 110-129.
    [61]
    Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine[J]. New England Journal of Medicine, 2023, 388(13): 1233-1239.
    [62]
    Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge[J]. Nature, 2023, 620(7972): 172-180.
    [63]
    Li Y, Li Z, Zhang K, et al. Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge[J]. Cureus, 2023, 15(6).
    [64]
    Kanjee Z, Crowe B, Rodman A. Accuracy of a generative artificial intelligence model in a complex diagnostic challenge[J]. Jama, 2023, 330(1): 78-80.
    [65]
    Wang X, Chen G H, Song D, et al. Cmb: A comprehensive medical benchmark in chinese[J]. arxiv preprint arxiv:2308.08833, 2023.
    [66]
    Cai Y, Wang L, Wang Y, et al. Medbench: A large-scale chinese benchmark for evaluating medical large language models[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(16): 17709-17717.
    [67]
    Zheng Y, Gan W, Chen Z, et al. Large language models for medicine: a survey[J]. International Journal of Machine Learning and Cybernetics, 2024: 1-26.
    [68]
    Omiye J A, Gui H, Rezaei S J, et al. Large language models in medicine: the potentials and pitfalls: a narrative review[J]. Annals of Internal Medicine, 2024, 177(2): 210-220.
    [69]
    AlSaad R, Abd-Alrazaq A, Boughorbel S, et al. Multimodal large language models in health care: Applications, challenges, and future outlook[J]. Journal of medical Internet research, 2024, 26: e59505.
    [70]
    Clusmann J, Kolbinger F R, Muti H S, et al. The future landscape of large language models in medicine[J]. Communications medicine, 2023, 3(1): 141.
    [71]
    Gumilar K E, Indraprasta B R, Hsu Y C, et al. Disparities in medical recommendations from AI-based chatbots across different countries/regions[J]. Scientific Reports, 2024, 14(1): 17052.
    [72]
    Wang D, Zhang S. Large language models in medical and healthcare fields: applications, advances, and challenges[J]. Artificial Intelligence Review, 2024, 57(11): 299.
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