D r A gent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025)

Abstract: Although large language models (LLMs) have demonstrated outperforming human experts in medical examinations, it remains challenging to adopt LLMs in real-world clinical decision-making that typically involves multi-hop medical reasoning. Common practices include prompting commercial LLMs and fine-tuning LLMs on medical data. However, in the clinical domain, using commercial LLMs raises privacy concerns regarding sensitive patient data. Fine-tuning competitive medical LLMs for different tasks usually requires extensive data and computing resources, which are difficult to acquire, especially in medical institutions with limited infrastructure. We propose DrAgent, which can build LLMs as agents to deliver accurate medical decision-making and reasoning. In implementation, we take a lightweight LLM as the backbone to collaborate with diverse clinical tools. To make efficient use of data, DrAgent introduces recursive curriculum learning to optimize the LLM in an easy-to-hard progression. The results show that our approach achieves competitive performance on diverse datasets.

## introduction recently, inspired by the impressive capabilities of large language models (llms) @xcite in understanding and generating human language, such as the gpt-series (ope-nai, 2023a) and the llama-series @xcite , the application of llms in healthcare to assist clinicians has attracted extensive research interest. existing efforts typically fine-tune publicly available llms, e.g., llama-series, on massive medical datasets to develop medical llms @xcite . representative examples include meditron @xcite , clinical camel @xcite , and pmc-llama @xcite . notably, med-gemini @xcite , developed by fine-tuning gemini @xcite on more than 7 million data samples, and gpt-4-medprompt @xcite , which prompts gpt-4 (openai, 2023a) with elaborately designed prompts, demonstrate performance surpassing that of experts on the united states medical licensing examination @xcite . however, these medical llms are typically evaluated on close-ended examination-style qa @xcite that does not reflect real-world clinical practice where multi-hop reasoning are required. for example, the process of treatment recommendations could involve the following: 1) clinicians need to analyze the patient's symptoms and history of present illness. 2) when a patient's condition is complex, preliminary examinations (e.g., physical examination or blood tests) are often required for further assessment. 3) sometimes, preliminary examinations do not provide enough information for an accurate diagnosis. in such cases, clinicians may order additional tests (e.g. ct scans or microbiology examinations) based on previous results, continuing until a reliable diagnosis can be made. 4) when clinicians recommend treatment plans, such as medications, they typically consider potential drug-drug interactions with the patient's current medications and account for the patient's specific circumstances (e.g. pregnancy) to evaluate possible side effects. thus, real-world clinical practice diverges from the structured nature of exam-taking that existing medical llms are evaluated on. existing research has shown that such llms perform poorly on complex clinical decision-making, e.g., medical code querying @xcite , new drug understanding @xcite , and clinical diagnostics @xcite . a potential solution for enabling llms to deliver accurate results across various medical tasks is fine-tuning llms on target medical data and figure 1 : a. the answer generated by gpt-4o (openai, 2023a); b. our dragent is organized into three components (thought, action, and observation) for collaborating with diverse medical tools to deliver effective multi-hop medical reasoning and decision-making. red-colored and green-colored text indicate the wrong and correct diagnoses and treatments, respectively. gpt-4o provides an answer in just one round of reasoning, which is usually neither appropriate nor reliable in real-world clinical decision-making, resulting in wrong diagnoses and treatment recommendations. in contrast, our dragent is designed, as shown in the above example, to make decisions. we propose an efficient solution, dragent, that can build lightweight llms as medical agents to collaborate with diverse medical tools, as shown in figure 1 . in implementation, we first build a tool library consisting of 16 diverse medical tools (as shown in during training, rcl enables llms to progressively learn multi-hop medical reasoning in an easyto-hard fashion and recursively learn experience from unseen cases, similar to the human learning curve: 1) it starts with simple medical reasoning (e.g., single-hop reasoning) data; 2) it then attempts to process more difficult medical reasoning data, where multi-hop reasoning is required; 3) it performs reasoning for unseen cases; and learns to incorporate the experience from unseen cases to further boost performance, i.e., the successfully reasoned cases are used for continued training, and the unsuccessfully reasoned cases can be used for reflection training. such a practice has been shown to be a better solution than the common approach of uniformly sampling training data from limited medical data @xcite . meanwhile, learning from unseen cases can reduce models' reliance on labeled data and enable the use of unlabeled data for training. overall, our method has the following advantages: ## related work medical large language models different medical llms @xcite ) that adapt general llms to the medical domain have been proposed to assist clinicians in decision-making @xcite @xcite . however, existing works usually ignore not only the real-world complex decisionmaking that involves multi-hop medical reasoning @xcite , but also the use of existing deployed medical tools to support reasoning. meanwhile, it has shown that llms are ineffective at dealing with complex clinical tasks @xcite . in this work, we propose the efficient dragent that collaborates with existing medical tools and enables llms to efficiently deal with a range of complex tasks. ai agents ai agents are designed to perceive their environment and make decisions to achieve specific goals @xcite . recently, the superior performance of llms has improved agents' capabilities in interacting with the environment using language @xcite . 1) prompting closed-source llms as agents: most existing works @xcite @xcite @xcite focus on using prompting techniques to leverage closed-source llms (such as gpt (openai, 2023a)) to make decisions. for example, medagents @xcite prompts gpt-4 in a role-playing setting to take on multiple different roles, such as cardiologist and pulmonologist, and then involves them in discussions. however, the heavy reliance on prompts for customized roles makes it difficult and unstable to customize the agent's behavior @xcite . additionally, deploying closed-source models in clinical settings raises privacy concerns over sensitive patient data. ## experiments in this section, we provide the main results and analyses to show the effectiveness of our approach. ## conclusions we have introduced dragent, a lightweight large language model designed to address critical challenges in adopting llms for real-world medical decision-making and reasoning. our approach emphasizes data efficiency, parameter efficiency, and compute efficiency, enabling effective collaboration with diverse clinical tools while overcoming privacy concerns and resource limitations commonly faced in the clinical domain. to alleviate the reliance on labeled training data, we propose recursive curriculum learning, which allows dragent to achieve desirable performance on complex medical reasoning tasks, even with limited data. experiments across different datasets, including multi-hop medical reasoning tasks, highlight the effectiveness of our method. overall, our work highlights the potential of hybrid systems that combine llms with validated medical tools, circumventing the computational and logistical barriers to training standalone ai models for every clinical scenario. by leveraging tools, we enhance the precision and reliability of ai-assisted decision-making while aligning with the practical needs of healthcare systems. our findings advocate for a collaborative model of ai deployment in medicine, emphasizing scalability, resource efficiency, and clinical accuracy.