Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning

## Abstract

Abstract Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited "out-of-the-box" capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a hybrid training paradigm combining supervised and reinforcement learning, we train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making. We evaluate our methodology on MIMIC-CDM, a real-world dataset covering four abdominal diseases containing various clinical tests and show the benefit of explicitly training clinical decision-making for increasing diagnostic performance and efficiency.

## 1 Introduction

In modern healthcare systems, accurate and timely diagnosis stands as a critical component in patient management and treatment Savioli et al. (2022). To form a diagnosis, clinicians often engage in clinical decision-making through a dynamic, iterative process, called differential diagnosis. This process involves forming hypotheses about the patient and testing those hypotheses through requesting and interpreting information from relevant available diagnostic tests Sox et al. (2024). At the beginning multiple diagnoses may be possible and there is still high uncertainty. Through diagnostic testing the uncertainty is minimized and the space of possible diseases reduced until a sufficient confidence is reached, a diagnosis can be given, and treatment can begin Sox et al. (2024). It is especially important in complex, high-stakes environments such as emergency departments, where often little is known about a patient at admission and fast and accurate diagnosis is paramount Savioli et al. (2022).

Figure: Figure 1: An illustrative process of clinical decision-making performed by LA-CDM. At the beginning, only the patient history including symptoms and family history is known. In a cyclic process, the hypothesis agent forms an uncertainty-aware hypothesis and the decision agent decides on a clinical action (request a test or diagnose). If a test is requested the results are added to the known patient information. The cycle repeats until a final diagnosis is given.
Refer to caption: x1.png

Large Language Models (LLMs), with their ability to synthesize complex textual information, seem well-suited for aiding clinicians in this task, as most medical information is textual or can be represented in text, e.g., clinical notes, imaging reports or numerical laboratory results. This enables great flexibility and variety in medical modalities. In the medical domain LLMs have already shown great success in passing medical license exams Singhal et al. (2023); Gilson et al. (2023) and diagnosing case challenges Buckley et al. (2023). The potential to revolutionize healthcare through accurate diagnostic capabilities is enormous, however, existing approaches often either (1) assume immediate availability of all patient data Buckley et al. (2023); McDuff et al. (2023); Chen et al. (2025), which is rarely the case in practice, or (2) rely on the often limited “out-of-the-box” behavior of pre-trained LLMs without any task-specific fine-tuning to the complexities of diagnostic decision-making Hager et al. (2024); Li et al. (2024). This mismatch between research and real-world clinical decision-making limits the applicability of LLMs to the clinical setting.

In this paper, we address the above limitations by modeling and training Language Agents for Clinical Decision Making (LA-CDM), tasked with iteratively reducing hypothesis uncertainty through repeated diagnostic testing. Inspired by cognitive research on human clinical decision-making Sox et al. (2024), we design a two-agent system replicating the two main cognitive tasks of clinicians involved in clinical decision-making. It consists of one LLM agent, the *hypothesis agent*, forming the most likely diagnosis hypothesis based on all available patient information and estimating its confidence in that hypothesis, and another agent, the *decision agent*, that evaluates the patient information and the hypothesis agent’s output to either provide a diagnosis or request an additional diagnostic test (Figure [1](https://arxiv.org/html/2506.13474v1#S1.F1)). To train this system, we propose a novel training strategy with three distinct objectives that target the core principles of successful clinical decision-making Sox et al. (2024):

- 1.
Accurate hypothesis generation: Using supervised fine-tuning, the hypothesis agent is trained to form a correct hypothesis. Since information on the patient is only uncovered step-by-step, the agent has to make use of limited information from various data sources.
- 2.
Hypothesis uncertainty estimation: Using reinforcement learning the hypothesis agent is trained to be well-calibrated in its verbalized uncertainty estimation. A well-calibrated model that is e.g. 60% certain on a hypothesis will be correct in 60% of cases.
- 3.
Efficient decision-making: Using reinforcement learning, the decision agent is trained to select the most informative next test and reach a diagnosis when sufficiently confident. The model gets rewarded for a final correct diagnosis, reinforcing the testing pathway that led to that diagnosis.

Analogously, to doctors graduating from medical school, LLMs have a strong medical knowledge foundation, but are not trained on performing clinical decision-making. Clinicians learn this skill through years of experience, pointing towards experience-based reinforcement learning as a prime paradigm for teaching clinical decision-making to LLMs. Further, since optimal testing pathways are not known, supervised learning of clinical decision-making is infeasible. The interplay of the three objectives results in the model learning which tests to request in order to increase the hypothesis confidence leading to an accurate diagnosis. This guides the model to request those tests that are most informative in a given situation. To the best of our knowledge, we propose the first method for explicitly training LLMs for clinical decision-making.

We evaluate our method on MIMIC-CDM Hager et al. (2024), a real-world dataset focused on four abdominal diseases that mirrors clinical workflows by modeling differential diagnosis through sequential test requests. Its standardized test naming and inclusion of lab results, notes, and imaging reports make it uniquely suited for training and evaluating LLM-based diagnostic reasoning. Our experiments demonstrate the benefit of explicitly training clinical decision-making. Notably, training reduces the required diagnostic tests, which has practical implications in reducing healthcare costs, diagnosis time, and patient discomfort Savioli et al. (2022). We show the benefit of our hypothesis-driven approach to clinical decision-making and demonstrate that the model adapts its testing procedure to the patient at hand, placing this work as a step towards patient-specific personalized differential diagnosis.

## 2 Related Work

Reinforcement Learning for Clinical Decision Making
Reinforcement learning has been explored for cost-efficient clinical decision-making based on tabular data. Yu et al. (2023) train SM-DDPO, a model that iteratively requests laboratory tests optimizing diagnostic performance and cost-efficiency. Their method features an imputation model, estimating missing (and not yet requested) laboratory tests and a classification model predicting the diagnosis. A policy network trained with Q-learning predicts the next action, i.e. which test to request or which diagnosis to give. They show improvements in efficiency at a similar diagnostic performance compared to baselines making use of all available information. However, as the method is only compatible with tabular data, it neglects many important medical modalities, like clinical notes or imaging reports, which are often crucial for diagnosis.

ED-Copilot Sun et al. (2024) employs a Language Model for encoding serialized patient laboratory values which is trained end-to-end with two MLPs, one predicting test requests, and one predicting the task of outcome severity. They train their method in two stages. First, they use supervised learning to teach the model to predict all tests in a pre-defined order. In a second training stage, they use reinforcement learning to finetune the model to reduce time cost of the testing regiment. They also report great reduction in testing time, while remaining similar in performance to baselines making use of all available information. While this method uses a language model as its encoding backbone, this language model is not directly requesting tests. Same as the previous method, they further only consider tabular laboratory values and therefore do not make use of valuable information in textual form.

Reflexion Shinn et al. (2023) addresses general LLM decision-making by introducing a zero-shot reinforcement learning approach. Rather than fine-tuning the LLM with traditional reinforcement learning, the method allows the model to iteratively attempt the same task. After each trial, a limited number of previous attempts and a numerical reward are added to the input context, guiding the next generation. While this is effective at general decision-making, this approach cannot be applied in the clinical context, where multiple diagnosis trials with the same patient and intermediate diagnosis correctness feedback are infeasible.

LLMs for Clinical Decision Making
LLMs have so far only been used as zero-shot methods for clinical decision support. Hager et al. (2024) place large "out-of-the-box" language models in an evaluation framework where they are tasked with interactively requesting diagnostic tests and diagnose patients. They show severe limitations of LLMs for clinical decision-making and report worse diagnostic performance than clinicians. Vaid et al. (2024) approach clinical decision-making with a tool-using LLM. Through zero-shot prompting, they provide the LLM with a number of available tools, e.g., a symptom tool for retrieving the symptoms, or a imaging study tool for getting any imaging reports on the patient. They evaluate various proprietary LLMs, with GPT-4 Achiam et al. (2023) showing the best performance.
Liu et al. (2024) model LLM clinical decision-making as a multi-agent setting, where a doctor agent communicates with a patient agent who can detail his symptoms and a technician agent who can perform laboratory or imaging results. They compare different prompting techniques, like chain-of-thought Wei et al. (2022) or one-shot prompting and show that GPT-4o achieves the best performance. All these methods do not attempt to improve model performance by explicitly training clinical decision-making with LLMs.

## 3 Preliminaries

### 3.1 Reinforcement Learning on Large Language Models

Reinforcement learning has emerged as a powerful framework for fine-tuning LLMs by aligning their behavior with desired objectives, especially when no clear ground truth for this behaviour exists. Most notably, Reinforcement Learning with Human Feedback (RLHF) Ouyang et al. (2022) was used to align LLM generations with human preferences. LA-CDM utilizes a direct reinforcement learning method based on Proximal Policy Optimization (PPO) Schulman et al. (2017) without requiring human feedback.

Let $\mathcal{X}$ be the space of textual inputs (prompts) and $\Theta\subseteq\mathbb{R}^{d}$ be the parameter space of a LLM. We denote by $\pi_{\theta}(a\mid s)$ the stochastic policy of the LLM, parameterized by $\theta$, which specifies the probability distribution over a discrete vocabulary $\mathcal{A}$ (the set of possible tokens) given a state $s\in\mathcal{S}$. In the context of language modeling, a state $s$ is typically a sequence of tokens $(x_{1},\ldots,x_{t})$ consisting of a prompt and previously generated tokens. An action $a$ is the next token to be generated, yielding the updated state $s{\prime}=(x_{1},\ldots,x_{t},a)$. Every state transition gives rise to an reward the model is trained to maximize.

### 3.2 Confidence Calibration of Large Language Models

While LLMs have shown impressive capabilities in many language-related tasks, hallucinations and confidently-presented wrong answers are a common and well-known problem Kadavath et al. (2022). A well-calibrated model is able to express confidence that aligns with the epistemic probability of correctness. This means that of all the answers which are presented with a confidence of $0\leq p\leq 1$, the fraction of correct answers is $p$.

In this work, we train confidence calibration with reinforcement learning as proposed by Stangel et al. (2025). They model confidence calibration as a betting game, where the model bets on the correctness of its answer. If it is correct with a high confidence it receives a large reward. However, if it is wrong with a high confidence, the punishment becomes large. Analogously, if the answer is wrong the model receives the largest reward if it expresses a low confidence. Concretely, they use the reward function

$$ $R(y_{pred},c,j)=\begin{cases}\log(c),&\textit{if $J(y_{pred})$ is True}\\ \log(1-c),&\textit{if $J(y_{pred})$ is False},\end{cases}$ $$

where $y_{pred}$ is the predicted answer, $0<c<1$ is the (scaled and clipped) confidence prediction, and $J(\cdot)$ is a binary function evaluating the correctness of $y_{pred}$. The reward is then scaled to be between $-1$ and $1$. They train the model using Proximal Policy Optimization (PPO) Schulman et al. (2017).
This training approach removes the need for an artificially constructed ground truth confidence dataset, as done by other confidence calibration methods Azaria and Mitchell (2023); Kadavath et al. (2022), and instead only requires a measure of answer correctness. The authors prove that an optimal policy under their reward design produces perfectly calibrated confidence expressions.

## 4 Language Agents for Clinical Decision Making

We propose LA-CDM consisting of two language agents, hypothesis agent and decision agent, trained with three different objectives. The hypothesis agent is trained in accurate hypothesis generation through supervised fine-tuning and uncertainty-awareness through reinforcement learning. The decision agent is trained in decision-making using reinforcement learning. Both agents share the LLM weights, so training one agent also influences the other. In Figure [2](https://arxiv.org/html/2506.13474v1#S4.F2), we show the full model. The two agents and the three training objectives will be explained in detail in this section.

Figure: Figure 2: Overview of our method LA-CDM and its three training objectives. The hypothesis agent receives the current patient state and predicts a hypothesis and confidence. The hypothesis generation is trained supervised, the confidence calibration using reinforcement learning. The hypothesis agent output and the current patient state are then provided to the decision agent that is trained to decide on an optimal clinical action (test request or diagnosis) using reinforcement learning.
Refer to caption: x2.png

### 4.1 Modeling Clinical Decision Making

Clinical Decision Making Environment
In order to train our model in decision-making it has to operate in a reinforcement learning environment to explore testing strategies and receive reward signals. The model learns decision-making through interacting with this environment and diagnosing patients. Let each patient be described by a number of $n$ test results $[t_{i}]^{n}_{i=1}$ as textual records of clinical notes, imaging reports and laboratory panels. Since patient information is iteratively uncovered through the model’s test requests $r_{j}$, we define the currently observed patient state at time-step $j$ as the set of all observed tests and write it as $p_{j}$. The correct diagnosis for the patient is denoted by $y_{true}$.

As we simulate a clinical patient-doctor interaction, $p_{0}$, the initial observed patient state, consists of the first clinical notes detailing symptoms, medical and family history. This information is always available to the model. The environment advances step-wise with each model action. If the model requests an additional test, the observed patient state is updated, and the results are made available to both the hypothesis agent and the decision-making agent for the next step. The simulation ends when the model provides a diagnosis for the patient or if one of the two failure cases is reached: (1) the model exceeds the specified maximum number of generated tokens, or (2) the model violates its specified output format.

Hypothesis Agent
Through its system prompt, the hypothesis agent is introduced to its task and provided with the possible diagnoses. At each time-step $j$ of the environment, the hypothesis agent $\mathcal{H}$ is given the currently observed patient state $p_{j}$ to predict the most likely diagnosis $h_{j}$ based on the limited available information, as well as the confidence in that prediction $c_{j}$. It therefore produces a mapping

$$ $\mathcal{H}:p_{j}\xrightarrow{}\{h_{j},c_{j}\}.$ $$

The model generates this output in a format of "Hypothesis: $h_{j}$, Confidence: $c_{j}$". The agent reports numerical confidence estimations on a scale of 0 0 to $10$, where $10$ means absolute certainty of the correctness of the hypothesis, and 0 0 means absolute certainty that it is incorrect.

Decision Agent
The decision agent $\mathcal{D}$ is the actor advancing the environment. It decides on which action to take at each time-step. Through its system prompt it is provided with its task, a list of tests present in the dataset, and the possible patient diagnoses. Provided with the currently observed patient state $p_{j}$ and the hypothesis agent’s hypothesis $h_{j}$ and confidence $c_{j}$, it produces a decision on whether to request another diagnostic test $r_{j}$ and move on to the next time-step $j+1$ or whether to commit on a specific diagnosis $y_{pred}$ for the patient and end the episode. Formally, it produces a mapping

$$ $\mathcal{D}:\{p_{j},h_{j},c_{j}\}\xrightarrow{}\begin{cases}r_{j}&\textit{if a further test is requested}\\ y_{pred}&\textit{if a diagnosis is given}\end{cases}$ $$

Specifically, we employ the ReAct prompting technique Yao et al. (2022), to prime the model to first produce a reasoning trace, following chain-of-thought principles Wei et al. (2022), and then provide action and action input (in our case, the specific test or diagnosis) in a structured format.

If a further test was requested, the test results are appended to the conversation context as a user response to the LLMs generation. Since we can only work with retrospective data, where not every test result is present for every patient, we cannot always fulfill the model’s request. In these cases, the user reply tells the model the requested test is unavailable and asks it to choose a different action. The implications of this will be discussed later on. Also if the model requests tests or provides diagnoses that are not on the list of possible tests or, respectively, diseases, the model is asked to choose a different action.

### 4.2 Training Clinical Decision Making

In our training objectives we follow the three main principles of clinical decision-making, as proposed by Sox et al. (2024): (1) accurate hypothesis generation, (2) hypothesis uncertainty estimation, and (3) efficient decision-making. The first two objectives are trained with respect to the generations of the hypothesis agent, the last objective is trained on the environment interactions of the decision agent.
We follow a cyclic training approach, where each objective is trained individually for a specified number of episodes after which the objective changes to the next one until the cycle repeats, resulting in much more stable training compared to optimizing all objectives simultaneously.

Training Hypothesis Generation
A baseline of good clinical decision-making is a high accuracy in hypothesis generation. If the model knows the most likely candidate for the diagnosis, it can adapt its testing strategy to quickly confirm or reject this hypothesis. While the model interacts with the environment, the hypothesis agent is confronted with various patient states consisting of different subsets of test combinations. We collect all contexts shown to the model within all episodes of a patient batch, usually including multiple hypothesis generation steps per patient. To perform supervised fine-tuning, we create target sequences, consisting of the collected conversation contexts concatenated with the correct hypothesis generation $y_{true}$. We compute the cross-entropy loss for the sequences, ignoring the token at the position where the model should place its confidence score.

Training Uncertainty-Awareness
Since the available patient information is often limited, especially at early stages of the diagnostic process, and the available data does not always clearly point at a specific diagnosis, uncertainty is inherent to clinical decision-making. An accurate intrinsic estimation of that uncertainty by the model can give it an improved basis for decisions on when to stop the diagnostic process and produce a diagnosis. In this work, we train confidence calibration following a method proposed by Stangel et al. (2025) as previously introduced in Section [3.2](https://arxiv.org/html/2506.13474v1#S3.SS2).
We define our correctness measure $J(h_{j})$ as equality between the predicted hypothesis $h_{j}$ and the ground truth diagnosis $y_{true}$. Since "out-of-the-box" pre-trained LLMs tend to only predict high confidences, the model rarely explores predicting low confidences, significantly hindering the reinforcement learning training. We take inspiration from stochastic reinforcement learning exploration techniques and, with probability $p_{explore}$, replace the predicted confidence with a randomly chosen different confidence. The exploration probability is scheduled to decrease during training.

Training Clinical Action Selection
At the core of training clinical decision-making lies the training of clinical action selection. During interaction with the clinical decision-making environment the model can freely choose to iteratively request any number of tests in any order. Given the vast number of possible diagnostic pathways, defining an optimal test sequence for each patient is infeasible, we therefore do not have a ground truth on which tests to perform. To still be able to train clinical decision-making, we propose to use reinforcement learning, where the model can learn through trial-and-error which tests are useful in which situations. Equal to the confidence calibration training, we employ the PPO algorithm Schulman et al. (2017) for reinforcement learning training. Through interaction with the clinical decision-making environment the model requests different tests until it decides on a diagnosis. We design our reward function to present the model with a fixed positive reward $r_{pos}$ if the final diagnosis at the end of the diagnosis episode was correct, or a fixed negative reward $r_{neg}$ if it is wrong. Additionally, we punish the model with reward $r_{invalid}$ if the model violates the specified format. Our reward function is thus:

$$ $R(y_{pred})=\begin{cases}r_{pos}&\textit{if $y_{pred}=y_{true}$}\\ r_{neg}&\textit{if $y_{pred}\neq y_{true}$}\\ r_{invalid}&\textit{for out-of-format generations}\end{cases}$ $$

The interaction of these three objectives enables the model to learn which tests to request to enhance hypothesis confidence, ultimately leading to a more accurate diagnosis. This drives the model to prioritize tests that provide the most informative insights in a given situation.

## 5 Experimental Set-up

### 5.1 Dataset and Pre-Processing

We evaluate our method on the MIMIC-CDM dataset Hager et al. (2024), a curated subset of MIMIC-IV Johnson et al. (2020) designed for modeling sequential clinical decision making. It contains 2,400 patients diagnosed with one of four abdominal conditions: appendicitis, cholecystitis, diverticulitis, or pancreatitis. This focused setting on four pathologies reflects real clinical workflows, where physicians perform differential diagnosis with a narrowed down space of possible diseases and request tests to distinguish between likely candidates. The dataset includes patient histories (symptoms, comorbidities, family histories) and physical exam notes for all patients. It also provides 5,959 textual imaging reports (CT, x-ray, ultrasound, MRI) and 143,191 lab results (blood, urine, microbiology), however, not every test result is reported for every patient. If multiple values for a specific test were recorded during the hospital stay of the patient only the first one was included in MIMIC-CDM to simulate an early diagnosis after hospital admission. Crucially, the dataset includes comprehensive mappings of test names across patients, an essential feature for modeling test requests reliably. Without this normalization, a model could not query the same test across different cases due to inconsistent naming in clinical documentation. To our knowledge, MIMIC-CDM is the only publicly available dataset that enables simulation of this setting.

For our use-case, we construct the set of available tests as: physical examination, all imaging modalities, and the most common laboratory panels. These panels are collections of individual tests that are usually ordered together. The initial patient history is shortened by processing them with a Mixtral-8x7B Jiang et al. (2024) model, prompted to summarize the most important aspects. Also the imaging reports are shortened, wherever a separation into sections was available, by only keeping the findings section of the report and removing the remaining sections.
The original dataset does not have a data split, since it was intended for the evaluation of zero-shot models. We therefore split the data into a training set of 80%, and a validation and test set of 10% each.

### 5.2 Metrics

To evaluate model performance, we report class-wise accuracies along with their mean, as well as micro and macro F1-scores. As classification is performed by auto-regressive LLMs, some generations do not include a valid class. If this is the case, we fall back to the last classification of the hypothesis agent. If the method under evaluation does not have a hypothesis agent, or that hypothesis agent also predicts an invalid class, those predictions are defined as predictions of a separate fifth wrong class, however, we exclude their contribution to the macro F1-score.

Additionally, we compute the Expected Calibration Error (ECE) to assess confidence calibration of our hypothesis agent. ECE measures the discrepancy between predicted confidence scores and actual accuracy. A lower ECE indicates better model calibration, meaning the predicted probabilities align well with actual correctness frequencies.

**Table 1: Performance comparison of LA-CDM and baseline methods. We report class-wise accuracies and F1-scores. The avg. # tests is the mean number of tests that were requested by the model for all patients in the test set. *OASST is evaluated on a different test set in a different framework. ^†SM-DDPO can only process tabular data. ZS = zero-shot.**
|  | Accuracy | F1-Score | Avg. |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | Append. | Cholec. | Divert. | Pancr. | Mean | Micro | Macro | # Tests |
| OASST Hager et al. (2024)* | 82.0 | 48.0 | 45.5 | 44.1 | 54.9 | - | - | - |
| SFT-all | 89.6 | 78.5 | 96.0 | 66.6 | 82.7 | 82.1 | 82.4 | 9.61 (all) |
| SM-DDPO Yu et al. (2023)^† | 74.3 | 0.0 | 15.6 | 58.0 | 37.0 | 45.4 | 31.8 | 6.46 |
| ReAct Yao et al. (2022) | 57.3 | 67.7 | 76.0 | 38.9 | 60.0 | 57.9 | 46.0 | 2.62 |
| LA-CDM (ZS) | 67.7 | 56.9 | 72.0 | 44.4 | 60.3 | 60.4 | 56.8 | 2.25 |
| LA-CDM | 93.8 | 80.0 | 60.0 | 59.3 | 73.3 | 80.4 | 77.4 | 0.65 |

### 5.3 Baselines

We compare our method to various zero-shot and trained baselines. In their work, Hager et al. (2024) introduce both the MIMIC-CDM dataset and evaluate how various "out-of-the-box" pre-trained models perform when tasked with clinical decision-making. We compare with OASST, the best performing model from this evaluation, however, a direct comparison is very difficult for multiple reasons. First, as a zero-shot method, the model was evaluated on the complete dataset, whereas we reserve some part of that dataset for training. Second, the clinical decision framework was constructed differently, as the OASST model is not provided with the possible diagnosis classes or available tests in its prompt. Additionally, as an approximate upper-bound for our method, we compare with a Llama-3-7B-Instruct model Dubey et al. (2024) identical to our LLM backbone, trained with supervised fine-tuning to predict the correct diagnosis. Instead of requesting tests it simply receives all the available patient information directly which is not a realistic diagnostic process. We refer to this method as SFT-all. We compare to three other adaptive test selection methods on MIMIC-CDM. SM-DDPO Yu et al. (2023) trains a MLP using reinforcement learning for clinical decision making, however, the method is only able to process tabular data and can only request laboratory values. ReAct Yao et al. (2022) is a zero-shot decision making method. Finally, we compare with an untrained zero-shot version of our method, LA-CDM (ZS), including hypothesis and decision agent.

## 6 Results and Discussion

Figure: Figure 3: Left: Calibration curves before and after training LA-CDM. Right: Distribution of confidence estimations before and after training LA-CDM.
Refer to caption: x3.png

**Table 2: Ablation study of the hypothesis-driven approach. HA = hypothesis agent, DA = decision agent.**
| Agents | Accuracy | F1-Score |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| HA | DA | Append. | Cholec. | Divert. | Pancr. | Mean | Micro | Macro | Avg. # Tests |
|  | $\checkmark$ | 88.5 | 72.3 | 48.0 | 44.4 | 63.3 | 70.0 | 73.6 | 2.20 |
| $\checkmark$ | $\checkmark$ | 93.8 | 80.0 | 60.0 | 59.3 | 73.3 | 80.4 | 77.4 | 0.65 |

### 6.1 Comparison with Baselines

Our comparison with baselines is shown in Table [1](https://arxiv.org/html/2506.13474v1#S5.T1). When comparing with OASST Hager et al. (2024), LA-CDM shows improvement in accuracy for each class resulting in almost 20 percentage points difference when comparing the mean of all class accuracies. The performance shows the largest improvement for pathologies that are less common compared with the majority class appendicitis. While the methods are not directly comparable as outlined in section [5.3](https://arxiv.org/html/2506.13474v1#S5.SS3), the high performance improvement clearly shows the advantage of training on the task of clinical decision-making and not relying on inherent capabilities of large pre-trained models. This is further supported by the evaluation results of our method as a zero-shot model. In comparison to our trained model, we see a great performance improvement achieved through training. Importantly, the zero-shot method also diagnoses less efficiently than the trained version, requiring on average almost 3.5x more tests to form a diagnosis. Also the individual performance of the hypothesis agent benefits from training. Through our hypothesis generation training, we improve the ability of the model to form correct hypotheses from 54.6% to 74.2%. We equally show an improvement of uncertainty-awareness through training the confidence calibration objective. The ECE decreases significantly from 0.226 to 0.150. We visualise the calibration curves and confidence distribution of the two models in Figure [3](https://arxiv.org/html/2506.13474v1#S6.F3), where the trained model shows better calibration, especially at often predicted confidences. It also shows more distributed confidence predictions, compared to the untrained model, which tends to be very over-confident, mostly predicting a confidence of 80%.

The SFT-all model serves as a rough upper bound, as it leverages all available retrospective patient data, an unrealistic setup for real-time clinical decision-making. Therefore, it cannot be used for direct patient interactions. LA-CDM performs comparably, trailing by only two and five percentage points in micro and macro F1-score, respectively, while requiring 15x fewer diagnostic tests. This substantial reduction in test count highlights the efficiency of our approach.

Moreover, we observe evidence of patient-adaptive testing strategies aligning with best practices: for suspected cholecystitis, the model most frequently selects ultrasound (32% of cases), the gold-standard test Hirota et al. (2007); for appendicitis, it prioritizes CT scans (8% of cases), consistent with diagnostic guidelines Di Saverio et al. (2020). These results demonstrate that our method not only achieves high diagnostic accuracy but also optimizes resource usage in a clinically meaningful way. The reasoning traces generated by chain-of-thought prompting further enable the interpretation of the model’s testing pathways. We report qualitative examples of the model’s generations in Appendix [C](https://arxiv.org/html/2506.13474v1#A3).

### 6.2 Ablation Study

We evaluate the benefit of our hypothesis-driven approach in Table [2](https://arxiv.org/html/2506.13474v1#S6.T2). When removing the hypothesis agent from our methodology, the decision agent has to learn to request tests and to diagnose without relying on the uncertainty-aware hypothesis generation capabilities of the hypothesis agent, explicitly trained for these objectives. The benefit of the hypothesis agent is demonstrated clearly by improvements in all metrics. Notably, including the hypothesis agent also significantly reduces the amount of tests requested by the model.

### 6.3 Limitations

This work presents a first step towards training LLMs for clinical decision-making. As such there are some limitations that should be addressed in future work. First, the data we are training on is limited. It only contains four abdominal pathologies and a limited number of available diagnostic tests, the model is therefore only trained in clinical decision-making for these diseases. An extension to more diseases and more tests remains as future work. Secondly, the data we are training on is retrospective with different tests missing for different patients. Furthermore, the available tests are those tests that the clinicians involved in treating that patient performed. The model can only explore a limited spread of testing pathways. It can therefore only learn to become more efficient within the testing protocols performed by doctors. Simulation of unavailable test data could open up a pathway for modeling a more holistic clinical decision-making environment.

## 7 Conclusion

In this work, we propose a novel approach to clinical decision-making using LLMs by explicitly modeling the iterative hypothesis refinement process that clinicians follow in practice. Unlike previous work that either assumes full access to patient data or relies on untrained, out-of-the-box LLM behavior, our method introduces a structured, two-agent system trained to dynamically acquire and integrate diagnostic information. By leveraging reinforcement learning to optimize uncertainty estimation and decision-making, we enable a model that not only improves diagnostic accuracy but also enhances efficiency in medical testing. Our evaluation on the MIMIC-CDM dataset demonstrates that our approach surpasses existing baselines, achieving more accurate diagnoses with fewer diagnostic tests. This reduction in testing needs has direct benefits for real-world healthcare settings, including lower costs, faster diagnosis times, and reduced patient burden. Furthermore, our findings highlight the ability of the model to adapt its testing strategy based on patient-specific information, an essential step toward personalized AI-assisted healthcare.

## Acknowledgements

The authors gratefully acknowledge the financial support by the Bavarian Ministry of Economic
Affairs, Regional Development and Energy (StMWi) under project ThoraXAI (DIK-2302-0002), and
the German Research Foundation (DFG, grant 469106425 - NA 620/51-1).

## References

- Achiam et al. (2023)
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.
Gpt-4 technical report.
*arXiv preprint arXiv:2303.08774*, 2023.
- Azaria and Mitchell (2023)
Amos Azaria and Tom Mitchell.
The internal state of an llm knows when it’s lying.
*arXiv preprint arXiv:2304.13734*, 2023.
- Buckley et al. (2023)
Thomas Buckley, James A Diao, Adam Rodman, and Arjun K Manrai.
Accuracy of a vision-language model on challenging medical cases.
*arXiv preprint arXiv:2311.05591*, 2023.
- Chen et al. (2025)
Zhen Chen, Zhihao Peng, Xusheng Liang, Cheng Wang, Peigan Liang, Linsheng Zeng, Minjie Ju, and Yixuan Yuan.
Map: Evaluation and multi-agent enhancement of large language models for inpatient pathways.
*arXiv preprint arXiv:2503.13205*, 2025.
- Di Saverio et al. (2020)
Salomone Di Saverio, Mauro Podda, Belinda De Simone, Marco Ceresoli, Goran Augustin, Alice Gori, Marja Boermeester, Massimo Sartelli, Federico Coccolini, Antonio Tarasconi, et al.
Diagnosis and treatment of acute appendicitis: 2020 update of the wses jerusalem guidelines.
*World journal of emergency surgery*, 15:1–42, 2020.
- Dubey et al. (2024)
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al.
The llama 3 herd of models.
*arXiv preprint arXiv:2407.21783*, 2024.
- Gilson et al. (2023)
Aidan Gilson, Conrad W Safranek, Thomas Huang, Vimig Socrates, Ling Chi, Richard Andrew Taylor, David Chartash, et al.
How does chatgpt perform on the united states medical licensing examination (usmle)? the implications of large language models for medical education and knowledge assessment.
*JMIR medical education*, 9(1):e45312, 2023.
- Hager et al. (2024)
Paul Hager, Friederike Jungmann, Robbie Holland, Kunal Bhagat, Inga Hubrecht, Manuel Knauer, Jakob Vielhauer, Marcus Makowski, Rickmer Braren, Georgios Kaissis, et al.
Evaluation and mitigation of the limitations of large language models in clinical decision-making.
*Nature medicine*, 30(9):2613–2622, 2024.
- Hirota et al. (2007)
Masahiko Hirota, Tadahiro Takada, Yoshifumi Kawarada, Yuji Nimura, Fumihiko Miura, Koichi Hirata, Toshihiko Mayumi, Masahiro Yoshida, Steven Strasberg, Henry Pitt, et al.
Diagnostic criteria and severity assessment of acute cholecystitis: Tokyo guidelines.
*Journal of hepato-biliary-pancreatic surgery*, 14:78–82, 2007.
- Hu et al. (2022)
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.
LoRA: Low-rank adaptation of large language models.
In *International Conference on Learning Representations*, 2022.
- Jiang et al. (2024)
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al.
Mixtral of experts.
*arXiv preprint arXiv:2401.04088*, 2024.
- Johnson et al. (2020)
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark.
Mimic-iv.
*PhysioNet. Available online at: https://physionet. org/content/mimiciv/1.0/(accessed August 23, 2021)*, pages 49–55, 2020.
- Kadavath et al. (2022)
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al.
Language models (mostly) know what they know.
*arXiv preprint arXiv:2207.05221*, 2022.
- Li et al. (2024)
Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan S Ilgen, Emma Pierson, Pang Wei Koh, and Yulia Tsvetkov.
Mediq: Question-asking llms and a benchmark for reliable interactive clinical reasoning.
In *The Thirty-eighth Annual Conference on Neural Information Processing Systems*, 2024.
- Liu et al. (2024)
Hongcheng Liu, Yusheng Liao, Siqv Ou, Yuhao Wang, Heyang Liu, Yanfeng Wang, and Yu Wang.
Med-pmc: Medical personalized multi-modal consultation with a proactive ask-first-observe-next paradigm.
*arXiv preprint arXiv:2408.08693*, 2024.
- McDuff et al. (2023)
Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu, Amy Wang, Jake Garrison, Karan Singhal, Yash Sharma, Shekoofeh Azizi, Kavita Kulkarni, et al.
Towards accurate differential diagnosis with large language models.
*arXiv preprint arXiv:2312.00164*, 2023.
- Ouyang et al. (2022)
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al.
Training language models to follow instructions with human feedback.
*Advances in neural information processing systems*, 35:27730–27744, 2022.
- Savioli et al. (2022)
Gabriele Savioli, Iride Francesca Ceresa, Nicole Gri, Gaia Bavestrello Piccini, Yaroslava Longhitano, Christian Zanza, Andrea Piccioni, Ciro Esposito, Giovanni Ricevuti, and Maria Antonietta Bressan.
Emergency department overcrowding: understanding the factors to find corresponding solutions.
*Journal of personalized medicine*, 12(2):279, 2022.
- Schulman et al. (2017)
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov.
Proximal policy optimization algorithms.
*arXiv preprint arXiv:1707.06347*, 2017.
- Shinn et al. (2023)
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao.
Reflexion: Language agents with verbal reinforcement learning.
*Advances in Neural Information Processing Systems*, 36:8634–8652, 2023.
- Singhal et al. (2023)
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al.
Large language models encode clinical knowledge.
*Nature*, 620(7972):172–180, 2023.
- Sox et al. (2024)
Harold C Sox, Michael C Higgins, Douglas K Owens, and Gillian Sanders Schmidler.
*Medical decision making*.
John Wiley & Sons, 2024.
- Stangel et al. (2025)
Paul Stangel, David Bani-Harouni, Chantal Pellegrini, Ege Özsoy, Kamilia Zaripova, Matthias Keicher, and Nassir Navab.
Rewarding doubt: A reinforcement learning approach to confidence calibration of large language models.
*arXiv preprint arXiv:2503.02623*, 2025.
- Sun et al. (2024)
Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu, and Chenyan Xiong.
ED-copilot: Reduce emergency department wait time with language model diagnostic assistance.
In *Forty-first International Conference on Machine Learning*, 2024.
- Vaid et al. (2024)
Akhil Vaid, Joshua Lampert, Juhee Lee, Ashwin Sawant, Donald Apakama, Ankit Sakhuja, Ali Soroush, Sarah Bick, Ethan Abbott, Hernando Gomez, et al.
Natural language programming in medicine: Administering evidence based clinical workflows with autonomous agents powered by generative large language models.
*arXiv preprint arXiv:2401.02851*, 2024.
- Wei et al. (2022)
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al.
Chain-of-thought prompting elicits reasoning in large language models.
*Advances in neural information processing systems*, 35:24824–24837, 2022.
- Yao et al. (2022)
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao.
React: Synergizing reasoning and acting in language models.
*arXiv preprint arXiv:2210.03629*, 2022.
- Yu et al. (2023)
Zheng Yu, Yikuan Li, Joseph Chahn Kim, Kaixuan Huang, Yuan Luo, and Mengdi Wang.
Deep reinforcement learning for cost-effective medical diagnosis.
In *The Eleventh International Conference on Learning Representations*, 2023.

## Appendix A Prompts

### A.1 Hypothesis Agent Prompt

You are an uncertainty-aware and helpful medical expert. Your task is to create a hypothesis for a diagnosis of a patient admitted to the emergency department complaining about abdominal symptoms. You can hypothesise one of the following four conditions:

appendicitis
cholecystitis
diverticulitis
pancreatitis

You have to base your hypothesis on the information on the patient that will be provided to you at the end. If you are uncertain, provide your best guess.
Additionally, indicate your confidence using a confidence score between 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. A value close to 0 means you think there is a high probability that the answer is wrong. The closer the value is to 10, the higher you think is the probability that the answer is correct. Be very fine-grained with your confidence estimation and make use of the whole number range.

####
Always use the following format for your answer:

Hypothesis: Here, you put your chosen condition as a single word. Nothing else goes here.
Confidence: Here, you put your confidence in this hypothesis on a scale of 0 to 10.

####
Here are the four master rules you need to follow:
1. Always provide a hypothesis, even if you are uncertain.
2. Always provide a confidence with full integers from 0 to 10.
3. Only give one of the four possible conditions as hypothesis and use their exact naming.
4. Do not answer with anything other than the hypothesis and confidence, do not give explanations, and follow the exact format given above.

Any violation of these rules, will have dire consequences for the patient. Any correct hypothesis will be greatly rewarded.

####
Here is some information on the patient:
{observed patient state}

### A.2 Decision Agent Prompt

You are an interactive, helpful medical expert. Your task is to diagnose patients admitted to the emergency department complaining about abdominal symptoms into the following four conditions:

appendicitis
cholecystitis
diverticulitis
pancreatitis

To aid you in your diagnosis, you can request the results for the following tests for the patient:

Physical Examination
CT
MRI
Radiograph
Ultrasound
Complete Blood Count
Basic Metabolic Panel
Comprehensive Metabolic Panel
Renal Function Panel
Liver Function Panel
Urinalysis
Electrolyte Panel

At the start you will be provided with some context on the patient.

In your diagnosis process always use the following format:

Thought: Here, you should think about what to do next. Be very brief.
Action: Here, you can either write "Diagnosis" if you are confident enough to provide a diagnosis or "Test" if you want to request a single test.
Action Input: If you chose diagnosis as your action, you should put the correct condition of the four provided above here. You MUST put a condition here. If you chose test, provide the test name from the list mentioned above. You can only request one single test per action. Use the exact naming of the test as given above. Do not add any specification, abbreviation or explanation to the test name. If you do this there will be detrimental consequences to the patient.
$<$submit$>$

If you requested a test, the user will provide you with the test results if available. Do not hallucinate the test results yourself. Don’t write any more text after that. If the test is not available, do not request the same test again.

####
Here are the four master rules:
1. Ask for exactly one test per test action.
2. Only ask for one of the tests given in the list above and do not ask for any test twice.
3. Use the exact naming of the tests as given in the list above without additional specifications of the region, test abbreviations, or explanations.
4. Be brief and concise in all your text.
5. Keep asking for tests until you are sufficiently confident but always provide a valid diagnosis in the end.

Any violation of these rules, will have dire consequences for the patient. Any correct diagnosis will be greatly rewarded.

####
Here are some examples:

1)
Thought: Given the symptoms, I should order a Physical Examination first.
Action: Test
Action Input: Physical Examination
$<$submit$>$

2)
Thought: The CT shows clear inflammation of the appendix.
Action: Diagnosis
Action Input: appendicitis
$<$submit$>$

####
Here is some context on the patient:
{observed patient state}
The current hypothesis is {hypothesis} with confidence {hypothesis confidence}.

## Appendix B Experiments with different LLM backbones

To show model family variability, we show performance comparisons of Qwen 2 7B instruct ([https://huggingface.co/Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)) and Llama 3.1 8B instruct ([https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)). The Qwen 2 model performs similarly to its Llama 3.1 counterpart, without changing any hyperparameters, highlighting the robustness of our approach. When evaluating smaller model scales, e.g., Qwen 2 1.5B instruct, we found a strong deterioration in performance and fomat-keeping difficulties, both zero-shot (ZS) and after training, showing the task’s difficulty. We report the results for the Qwen 2 8B instruct model in the Table [3](https://arxiv.org/html/2506.13474v1#A2.T3).

**Table 3: Performance comparison of LA-CDM with different LLM backbones. We report class-wise accuracies and F1-scores. The avg. test count is the mean number of tests that were requested by the model for all patients in the test set. All models are the instruct variants. ZS = zero-shot.**
|  | Accuracy | F1-Score | Avg. |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | Append. | Cholec. | Divert. | Pancr. | Mean | Micro | Macro | # Tests |
| Qwen 2 8B (ZS) | 87.5 | 66.2 | 84.0 | 53.7 | 72.8 | 73.8 | 56.3 | 1.52 |
| Llama 3.1 7B (ZS) | 67.7 | 56.9 | 72.0 | 44.4 | 60.3 | 60.4 | 56.8 | 2.25 |
| Qwen 2 8B | 91.7 | 69.2 | 56.0 | 77.8 | 73.7 | 79.6 | 75.8 | 0.47 |
| Llama 3.1 7B | 93.8 | 80.0 | 60.0 | 59.3 | 73.3 | 80.4 | 77.4 | 0.65 |

## Appendix C Qualitative Examples

We show exemplary LA-CDM decision-making procedures. In Figure [4](https://arxiv.org/html/2506.13474v1#A3.F4) and Figure [5](https://arxiv.org/html/2506.13474v1#A3.F5), we show two successful predictions. In Figure [6](https://arxiv.org/html/2506.13474v1#A3.F6), we show a failure case where the model gives a diagnosis prematurely without providing a reason, probably due to an overly confident hypothesis. A second type of failure case involves the model indefinitely requesting more and more tests until length limitations are reached and the decision-making process is aborted by the environment.

Figure: Figure 4: A successful diagnosis performed with the evaluation of Complete Blood Count, ultrasound, and CT. The model decides to confirm an initial suspicion with an ultrasound, however, the results are not conclusive, prompting another imaging test.
Refer to caption: x4.png

Figure: Figure 5: A short example diagnosis. In many cases, the model learns to correctly predict the condition immediately from the patient history without requiring further tests.
Refer to caption: x5.png

Figure: Figure 6: An example of a failed diagnosis. AFter the ultrasound the hypothesis agent is overly confident. The decision agent immediately diagnoses wrongly without providing a reason.
Refer to caption: x6.png

## Appendix D Implementation Details

We use a PyTorch implementation of the Llama-3-7B-Instruct model Dubey et al. [2024] provided by unsloth as base LLM and fine-tune it using LoRA Hu et al. [2022]. We train our method in a cyclic training regiment, starting with one epoch, consisting of 959 steps, of confidence calibration training with a exploration probability of 0.9, decaying by 0.5% each step, for ingraining an initial uncertainty awareness of the model. Following this, we continue with each objective for 50 steps in the order of (1) clinical action training, (2) hypothesis generation training, and (3) confidence calibration training. All objectives are optimized using the Adam optimizer with a learning rate of $1e-5$ and a batch size of 2 on a single NVIDIA A40 GPU until convergence on the validation set, taking approximately 24 hours.

## Appendix E Societal Impact

The development of LA-CDM represents a step forward in AI-driven clinical decision support, offering a structured and hypothesis-driven approach to diagnosis. By iteratively requesting and interpreting clinical tests, LA-CDM has the potential to improve diagnostic accuracy while optimizing resource utilization, ultimately enhancing patient outcomes.

From an ethical perspective, the deployment of LA-CDM necessitates rigorous safeguards to ensure patient safety, fairness, and transparency. As an AI system designed to support medical decision-making, it is crucial that its recommendations remain interpretable and aligned with established medical guidelines. Moreover, biases present in training data must be carefully monitored to prevent disparities in diagnostic performance across different patient populations.

In the broader societal context, LA-CDM has the potential to support clinicians in environments with high cognitive load, such as emergency departments and primary care settings, where time-sensitive decision-making is critical. However, it is essential that such AI-driven tools are positioned as augmentative rather than substitutive, ensuring that they enhance rather than diminish the role of healthcare professionals. Additionally, accessibility must be prioritized to ensure equitable deployment across diverse healthcare systems, including under-resourced settings.

## Appendix F Asset Licenses

In this work we employ Llama 3.1 8B instruct ([https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)) under the Llama 3.1 community license, Qwen 2 7B instruct ([https://huggingface.co/Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)) under the apache-2.0 license, and Mixtral 8x7B instruct ([https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)) under the apache-2.0 license.
The dataset MIMIC-CDM Hager et al. [2024] is published under the PhysioNet Credentialed Health Data License 1.5.0.

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 4
presentation: 3
questions: See above
soundness: 3
strengths: 1. The paper addresses an important gap in clinical AI systems by modeling the iterative nature of diagnostic reasoning.
2. The paper provides thorough comparison against multiple baselines and includes cost-efficiency metrics.
3. The paper attempts to integrate uncertainty estimation with sequential decision-making in a clinical context.
4. The paper includes detailed prompts and implementation details that facilitate understanding of the approach.
summary: This paper proposes LA-CDM, a two-agent system combining hypothesis generation and decision-making modules trained with hybrid supervised and reinforcement learning for iterative clinical diagnosis.
weaknesses: 1. The paper introduces separate hypothesis and decision agents without sufficiently justifying their architectural separation. Specifically:
- The hypothesis agent generates diagnostic hypotheses and confidence scores, while the decision agent uses this output to select tests or final diagnoses. However, both agents inherently engage in diagnostic reasoning, and the decision agent could potentially internalize hypothesis generation and confidence estimation.
- The confidence score produced by the hypothesis agent could be implicitly learned by the decision agent through its reasoning process, eliminating the need for explicit separation. This raises questions about whether the dual-agent design introduces unnecessary complexity.

2. The reinforcement learning component is trained on retrospective clinical data, which poses significant off-policy learning challenges:
- Historical data reflects actions taken by clinicians, which may not align with the optimal policy learned by the agent. This can lead to distributional shift and biased policy evaluation, as the agent may propose test sequences not represented in the data.
- The paper mentions handling unavailable tests by prompting the agent to choose alternatives, but this does not address the core issue of learning from a fixed dataset with limited action coverage. Off-policy correction methods (e.g., importance sampling, conservative Q-learning) or imitation learning techniques are not discussed.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. How robust is LA-CDM when scaling to new diseases or hospital systems?  
2. What is the impact of unavailable tests on exploration and policy stability?  
3. Why does cyclic training outperform joint optimization—any gradient conflict analysis?  
4. Does better hypothesis calibration improve decision timing and stop behavior?  
5. How sensitive is the cost trade-off to regional price variations?  
6. Can hard safety constraints be added to the RL objective?  
7. Have clinicians evaluated qualitative reasoning traces for plausibility?
soundness: 3
strengths: 1. **Innovative design:** Two-agent division closely reflects clinical reasoning loops.  
2. **Calibration reward:** Improves reliability of verbal confidence estimates.  
3. **Cost-sensitive optimization:** Demonstrates efficiency improvements without sacrificing accuracy.  
4. **Transparent methodology:** Prompts, cost tables, and training configs are public.  
5. **Multimodal context:** Integrates notes, labs, and imaging text within a unified environment.  
6. **Clear ablations:** Each module’s contribution is empirically validated.
summary: This paper proposes **LA-CDM (Language Agents for Clinical Decision Making)** — a two-agent LLM system for **iterative, hypothesis-driven diagnosis**.  
A **Hypothesis Agent** generates provisional diagnoses with calibrated confidence, while a **Decision Agent** chooses whether to request additional tests or finalize a diagnosis.  
Training uses a **hybrid pipeline**:
1. **SFT** on labeled cases for hypothesis quality.
2. **Reinforcement Learning (GRPO)** for calibrating confidence via a “betting” reward.
3. **RL** for cost-aware decision optimization, balancing diagnostic accuracy and test cost.

The environment is based on **MIMIC-CDM**, including text notes, imaging summaries, and lab data for 2,400 cases across four abdominal conditions.  
Results show LA-CDM achieves **higher accuracy and lower average testing cost** than ReAct or untrained baselines, with **better calibration (ECE ↓ from 0.069 to 0.037)**.
weaknesses: 1. **Limited dataset scope:** Only four conditions; no cross-domain or cross-hospital validation.  
2. **Environment artifacts:** Some requested tests are unavailable, possibly biasing learning.  
3. **Weak baselines:** Comparison methods are not fully aligned in modality or setting.  
4. **Preprocessing bias:** Summarization of clinical notes may distort reasoning cues.  
5. **Safety unaddressed:** No human-in-the-loop validation or fail-safe mechanism.  
6. **Ablation granularity:** Lacks exploration of cyclic vs. joint training and reward sensitivity.

---

confidence: 4
contribution: 3
initial_rating: 4
presentation: 2
questions: 1.How does LA-CDM generalize to other institutions/specialties or non-abdominal conditions? Any plans for external-site validation or leave-one-hospital evaluation?
2.Does summarizing histories and trimming imaging reports change diagnostic difficulty or shift feature reliance? Please provide with/without pre-processing results.
3.Do you quantify how often “test unavailable” occurs and its effect on learning/inference? Could synthetic imputation or simulation reduce bias from missing tests?
4.How sensitive are policies to the cost table (e.g., ±x% scaling, switching to a different hospital’s charges)? Please report accuracy/cost under several cost schedules.
soundness: 3
strengths: 1. The paper's theme is based on real-world clinical problems. The design of the two agents reflects the actual diagnostic process: hypothesis formation → targeted testing → decision-making.
2.The paper demonstrates a strong focus on cost. The R_cost term explicitly penalizes expensive tests (e.g., MRI USD4,866 vs CBC USD71), driving parsimonious testing.
3.The paper has principles for handling uncertainty. Confidence calibration via RL (betting-style reward) trains the model to express calibrated confidences without ground-truth confidence labels.
4.The paper aligns with the guidelines for patient adaptive behaviors.The trained model preferentially orders ultrasound for cholecystitis (64.9%) and CT for appendicitis (85.1%), matching clinical practice.
summary: This paper introduces LA-CDM, a two-agent LLM framework for clinical diagnosis that mirrors clinicians’ workflow: a hypothesis agent proposes a diagnosis with an explicit confidence, and a decision agent either orders the next test or finalizes the diagnosis. The authors train three objectives in a cyclic schedule—(1) supervised hypothesis generation, (2) RL-based confidence calibration (a betting-style reward), and (3) RL for action selection with a test-cost penalty—so the system learns to raise hypothesis confidence while minimizing unnecessary testing. Evaluated on MIMIC-CDM (2,400 patients; four abdominal diseases; labs, notes, imaging reports), LA-CDM improves mean class accuracy to 81.3% with macro-F1 81.3 and reduces average test cost to USD1,295.61 versus a zero-shot ReAct baseline at USD1,480.32; an ablation shows the cost reward cuts cost from USD1,427.85 to USD1,295.61 with similar accuracy. Calibration also improves (ECE 0.069 → 0.037).
weaknesses: 1.This study's evaluation is limited to four abdominal diseases based on a single retrospective dataset; it lacks broader disease coverage or external validation. It lacks generalizability.
2.The data in the paper has limitations in retrospective constraints and test availability bias. Many requested tests are unavailable in logs, forcing the agent to try alternatives; this caps exploration to clinician-observed pathways and can bias learned policies.
3.The paper preprocesses patient information.Summarization of histories and imaging reports (e.g., keeping only findings) could remove cues clinicians use, but the impact isn’t quantified.
4.The paper did not analyze the sensitivity of the cost model.Test costs are tied to a single hospital’s 2025 charge schedule; robustness to alternative cost tables or scaling is not reported.


Decision: Accept (Poster)