Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024)

Abstract: This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems’ foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.

## introduction dialogue systems for the medical domain, which are designed to converse with patients to obtain additional symptoms, make a diagnosis and recommend a treatment plan automatically @xcite @xcite . medical dialogue systems have significant potential to simplify the diagnostic procedure and reduce the cost of collecting information from patients, thus containing alluring application value and attracting academic and industrial attention @xcite . existing medical dialogue systems have played an important role in diagnosis @xcite , monitoring @xcite , intervention @xcite , counselling @xcite , education @xcite , and etc. to meet these real meets, retrieval @xcite , generation @xcite @xcite , and hybrid @xcite methods are applied for building medical dialogue systems. specifically, retrieval-based methods select appropriate responses from a prebuilt index, generation-based methods respond in a generative manner, and hybrid methods combine both approaches, using retrieval for efficiency and generative methods for flexibility. recently, the revolutionary progress in large language models (llm) @xcite @xcite has catalyzed substantial technological transformations in dialogue systems. llms are sophisticated neural network-based systems that have been trained on vast amounts of text data, enabling them to generate human-like responses and achieve remarkable accuracy, thus reshaping medical dialogue systems' foundation. despite the potential performance in the medical question-answering, there remains a translational gap @xcite foot_0 between cutting-edge techniques and realistic requirements in various medical scenarios. for example, in figure 2 , llms operate in a question-and-answering manner, instead of diagnosing like doctors, which may lead to patients being unable to obtain precise diagnostic results and effective treatment strategies. to move towards closing this gap, this work (a) summarizes the system categories, methods, and evaluation of medical dialogue systems, (b) analyzes the current issues and challenges of medical dialogue systems, then (c) attempts to provide po-medical dialog system system categories ( §2) functions diagnosis ks-ds @xcite , knse @xcite , disc-medllm @xcite , bentsao @xcite intervention trik @xcite , lekbot @xcite , asd @xcite monitoring hear me out @xcite , dr. youth @xcite , i hear you i feel you @xcite counselling psychiatric counseling @xcite , @xcite , woebot @xcite figure 1 : the main content flow and categorization of this survey. in the existing literature, medical dialogue systems have been discussed and surveyed @xcite @xcite @xcite @xcite . this survey differs from these surveys in two aspects. first, our survey is a systematic review from a rigorous technical perspective, summarizing methods before llm, and llm-based methods. second, this survey highlights the grand challenges of current medical dialogue systems, including medical specialization @xcite and multi-disciplinary treatment @xcite , which may inspire further research. this survey is organized as follows: section 2 introduces the system categories of medical dialogue systems, followed by the methods before llm, llm-based methods, and evaluation of medical dialogue systems in section 3, section 4, and section 5, respectively. finally, we summarize the major challenges and possible solutions for further work in section 6. the contributions of this paper are as follows: patient: thanks. doctor: drug therapy is recommended. soak your hands in warm water for half an hour, then apply dexamethasone cream. patient: i have excessive sweating and dry peeling in hands and feet. in summer, skin peeling is serious and hands are sweaty. chatgpt: i'm sorry to hear that. excessive sweating and dry peeling in hands and feet can be uncomfortable and inconvenient. here are some suggestions that may help: 1. use antiperspirant: … 2. wear breathable clothing: … 3. soak your hands and feet in warm water: … 4. moisturize your skin: ... 5. avoid hot and spicy foods: ... 6. see a doctor: ... tempt to organize medical dialogue resources 2 including open-source implementations, corpora, and paper lists, which may help new researchers quickly adapt to this field. ## system categories this section briefly summarizes the functions and techniques of current medical dialogue systems. specifically, the functions can be divided into five categories according to the dominant subjects, including doctors (diagnosis, intervention), patients (monitoring, counseling), and medical students (medical education). ## methods before llm in this section, we briefly summarize the medical dialogue systems before the emergence of llm from the technical aspect of publicly available resources. the methods can be mainly divided into three categories, retrieval-based methods, generation-based methods, and hybrid methods @xcite . ## large language model-based methods llms have generated significant interest due to their remarkable performance in understanding instructions and generating human-like responses. this section summarizes the medical dialogue methods based on llms. current llmsbased methods can be divided into two categories, prompting, and fine-tuning general llms. typical medical llms are listed in ## grand challenge our summarization of the medical dialogue system inspires us to redesign a wide spectrum of aspects. this section summarizes current challenges for medical dialogue systems. ## conclusion the exploration of medical dialogue systems has come a long way, yet the journey towards seamless integration with healthcare is far from over. the convergence of categories, methods, and metrics reviewed here showcases the field's maturity. with the advent of llms, we stand on the cusp of unprecedented advancements. however, the challenges highlighted underscore the need for continuous innovation and interdisciplinary collaboration. the future of medical dialogue systems hinges on our ability to navigate these complexities, ensuring that these technologies serve as a catalyst for improved patient care and medical research. researchers should embrace these trends with a commitment to ethical, inclusive, and transformative healthcare solutions.