Synthetic A rabic Medical Dialogues Using Advanced Multi-Agent LLM Techniques (2024)

Abstract: The increasing use of artificial intelligence in healthcare requires robust datasets for training and validation, particularly in the domain of medical conversations. However, the creation and accessibility of such datasets in Arabic face significant challenges, especially due to the sensitivity and privacy concerns that are associated with medical conversations. These conversations are rarely recorded or preserved, making the availability of comprehensive Arabic medical dialogue datasets scarce. This limitation slows down not only the development of effective natural language processing models but also restricts the opportunity for open comparison of algorithms and their outcomes. Recent advancements in large language models (LLMs) like ChatGPT, GPT-4, Gemini-pro, and Claude-3 show promising capabilities in generating synthetic data. To address this gap, we introduce a novel Multi-Agent LLM approach capable of generating synthetic Arabic medical dialogues from patient notes, regardless of the original language. This development presents a significant step towards overcoming the barriers in dataset availability, enhancing the potential for broader research and application in AI-driven medical dialogue systems.

## introduction healthcare is a crucial aspect of daily life, involving coordination among multiple specialized departments such as pharmacy, imaging, labs, and billing, alongside primary care provider notes. each patient interaction results in a clinical note documenting the discussions, medical conditions, and future plans, essential for continuous care and communication with patients and the healthcare team @xcite . unlike regular meeting summaries, clinical notes are semi-structured, including concise, bullet-pointed phrases with medical terminology and references to external data from electronic medical records @xcite . ai integration in healthcare promises significant improvements in efficiency and patient outcomes through enhanced data processing. in the arab world, ai can bridge gaps in healthcare delivery by automating medical dialogue generation from clinical notes, reducing the documentation burden on healthcare providers. this automation ensures comprehensive and real-time medical records, enhancing patient care quality. however, developing ai technologies for arabic medical dialogue generation faces challenges. arabic's morphological richness and dialect diversity complicate nlp tool development. the lack of publicly available medical datasets in arabic and privacy concerns further impede progress, requiring robust data handling protocols. many medical dialogue datasets available in english and translating them is not sufficient to capture the nuanced context-specific expressions and cultural subtleties of regional arabic dialects. google translate and similar automated systems primarily focus on modern standard arabic (msa) rather than regional dialects, which can result in the loss of these important linguistic features. these dialects encapsulate unique social contexts that automated systems often overlook, thereby diminishing the authenticity and effectiveness of the communication. moreover, translating dialects through such systems can strip away vital cultural aspects that are intrinsic to understanding and using the language properly. in contrast, advanced language models can adeptly adapt to various dialects, including the najdi dialect, to generate dialogues that are both accurate and contextually appropriate. utilizing the najdi dialect in synthetic dialogues ensures that the interactions closely mirror real-life conversations, thus providing more realistic and practical training scenarios for medical professionals. the use of dialect-specific expressions and idioms can resonate more deeply with native speakers, enhanc-ing the clarity and empathy of medical communication compared to msa. this approach not only improves the precision of conveyed information but also fosters a more genuine connection in medical interactions. recent efforts focus on advanced multi-agent large language models (llms) to generate synthetic arabic medical dialogues. these models, trained on diverse datasets, achieve high linguistic and medical accuracy. synthetic data generation offers a solution to the scarcity of arabic medical training data, providing feasible alternatives to real conversations and aiding in training robust ai models. this approach ensures ai systems remain adaptable and continue evolving with medical advancements and changing healthcare practices. our work contributes significantly to the field of arabic natural language processing (nlp) and ai-driven healthcare solutions through several innovative approaches: this study establishes a comprehensive evaluation framework for the generation tasks, using both quantitative metrics such as rouge and bertscore and qualitative assessments by domain experts. this dual approach ensures that the generated conversations adhere to technical accuracy and maintain practical usability and relevance in clinical settings. the generated medical dialogue dataset is accessible via hugging face 1 1 the generated medical dialogue dataset is available 2 related work the adoption of electronic health records (ehrs) in the arab world has enhanced health information availability and interoperability @xcite , but increased documentation workloads for clinicians. many find ehr documentation more time-consuming than traditional methods, leading to delays and incomplete records. the quality of electronic notes is often questioned due to readability and completeness issues, and the use of copy-and-paste practices @xcite . clinicians sometimes dictate notes during patient visits, reducing patient interaction and perceived empathy @xcite . employing medical assistants or scribes can help but requires significant investment and faces high turnover @xcite . automatic summarization technologies have gained interest due to improvements in speechto-text technologies, widespread ehr implementation, and ai advancements, particularly transformer models @xcite . early applications used statistical machine translation, recurrent neural networks (rnns), and advanced transformer-based models @xcite @xcite . recent efforts in arabic text summarization include tailored transformer models like aragpt2 @xcite , arabert (antoun et al., 2020a), arat5 @xcite , and arabic pegasus @xcite . these models process sequential data and capture arabic language complexities, excelling in both extractive and abstractive summarization tasks. the field faces challenges due to the lack of publicly available medical conversation datasets essential for training and evaluating these systems @xcite . this lack of data stems from the personal and sensitive nature of medical recordings. while private entities may develop their datasets, it limits open comparison of algorithms and outcomes. advances by large language models like gpt-4, claude, and gemini show promise, but the lack of common datasets restricts comprehensive evaluation in arabic nlp @xcite . specialized datasets have significantly advanced arabic nlp, enhancing language understanding and processing. @xcite introduced the on hugging face through this https://huggingface.co/ datasets/mars203020/arabic_medical_dialogue largest arabic corpus, sourced from over 500 gb of text, improving language modeling and fine-tuning advanced arabic models like those based on gpt-3 architecture. this corpus improved performance in benchmark tests by 4.5% to 8.5% over the multilingual bert model @xcite . the ags dataset by @xcite for abstractive text summarization, with 142,000 article-summary pairs, supports high-quality text summarization technologies. in medical arabic nlp, hammoud et al. (2021) developed a dataset for arabic medical text classification with 2,000 documents across 10 disease categories, supporting machine learning models for arabic medical data @xcite . despite advancements, challenges persist in dataset availability and accessibility, highlighting the need for collaborative efforts to develop and share domain-specific datasets publicly. this is crucial for applications like generating datasets from clinical notes to test arabic text summarization @xcite . ## automatic evaluation to evaluate the quality and accuracy of the generated arabic medical dialogues, we employed two primary metrics: rouge @xcite and @xcite . additionally, we compared the generated arabic results with the aci-benchmark dataset. the following sections detail the evaluation criteria and processes used. ## human evaluation the expert evaluation involved five medical practitioners assessing whether machine-generated conversations matched real patient-physician encounters, with a focus on medical common sense, knowledge, and logic. each evaluator was assigned 10 generated conversations and reviewed them against the corresponding clinical notes. they rated the dialogues using a likert scale (1-5), as outlined in the criteria for evaluation included whether the conversation's symptoms, complaints, medical history, diagnosis, and treatment were consistent with the clinical notes. additionally, they assessed the clarity and smoothness of the conversation's structure and flow. evaluators also considered if the conversation provided comprehensive answers to the patient's queries or concerns and offered appropriate patient advice. they examined adherence to ethical guidelines and standards for health communication and evaluated whether the language and medical terminology were suitable for the patient. effective communication and rapport, demonstrated through active listening, clear explanations, empathy, and respect, were also key factors. in addition to the ratings, evaluators provided feedback on what they liked and disliked about the dialogues and suggested areas for improvement. this feedback was used to determine the overall quality and effectiveness of the machine-generated dialogues, ensuring they meet professional standards for patient-physician interactions. ## discussion the results demonstrate the performance of our multi-agent llm system in generating arabic medical dialogues when compared to english dialogues from the aci-benchmark dataset. ## conclusion the study explores using a multi-agent llm system to generate arabic medical dialogues from english clinical notes. while promising in generating accurate and engaging dialogues, further refinement is needed. comparing these dialogues with english ones from the aci-benchmark dataset and evaluation results from claude-3-opus and gpt-4-turbo highlights areas for improvement, especially in translation and contextual consistency. a limitation is the synthetic nature of the aci-benchmark dataset, which may not reflect real-world clinical diversity. future research should address these issues by developing a cross-lingual similarity measure for longer texts and medical terms in arabic. we are making the dataset publicly available and expanding it with other clinical notes to ensure a more comprehensive and representative dataset. improving these aspects will enhance patient-provider communication in arabic-speaking regions.