SICAR at RRG2024: GPU Poor's Guide to Radiology Report Generation (2024)

Abstract: Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Our solution employs a lightweight multimodal language model (MLLM) enhanced with a two-stage post-processing strategy, utilizing a Large Language Model (LLM) to boost diagnostic accuracy and ensure patient safety. We introduce the "First, Do No Harm" SafetyNet, which incorporates X-Raydar, an advanced X-ray classification model, to crossverify the model outputs and specifically address false negative errors from the MLLM. This comprehensive approach combines the efficiency of lightweight models with the robustness of thorough post-processing techniques, offering a reliable solution for radiology report generation. Our system achieved fourth place on the F1-Radgraph metric for findings generation in the Radiology Report Generation Shared Task (RRG24).1

text: Proceedings of the 23rd Workshop on Biomedical Language Processing, pages 635–644 August 16, 2024. ©2024 Association for Computational Linguistics
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heading: 1 Introduction
text: Radiology is indispensable in healthcare, offering non-invasive methods to diagnose and monitor medical conditions. Central to this practice are radiology reports, which provide detailed interpretations of medical images crucial for clinical decision-making (Mityul et al., 2018). However, writing these reports is a meticulous process that demands significant domain expertise (Hartung et al., 2020). Radiologists must manually review images and formulate descriptive narratives, a task that is
1https://stanford-aimi.github.io/RRG24/
not only time-consuming but also susceptible to variability and errors, potentially affecting patient care and outcomes (Alexander et al., 2022).
One of the primary challenges in radiology report writing is the sheer volume of imaging studies that radiologists must interpret (Bruls and Kwee, 2020; Zhan et al., 2020). With the increasing use of imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and Xray, radiologists are facing a growing workload that exceeds their capacity to provide timely and accurate reports (Winder et al., 2021; Bruls and Kwee, 2020). This challenge is further compounded by the rising demand for imaging services due to an aging population and the increasing prevalence of chronic diseases.
Another imperative issue in radiology report generation is the variability in report quality and consistency (Minn et al., 2015; Pool and Goergen, 2010). Different radiologists may interpret the same set of images differently, leading to inconsistencies in the information provided in the reports. This variability can stem from differences in writing styles, experience levels, and individual biases, all of which can have significant implications for patient care (Plumb et al., 2009; Naik et al., 2001; Brady et al., 2012). Inconsistencies in reports may lead to missed diagnoses or incorrect treatment decisions, underscoring the importance of standardized and automated approaches to report generation.
To address these challenges, Automated systems
have the potential to enhance the efficiency and accuracy of radiology report generation (Liao et al., 2023; Pang et al., 2023; Liu et al., 2023). These systems can reduce the time and effort required by radiologists while standardizing reporting practices to ensure consistency and relevance in reports. Moreover, automation can help address the increasing workload and demand for imaging services.
As Large Language Models (LLMs) have become widely available, numerous studies have explored the development of Multimodal LLMs (MLLMs) capable of natively processing additional modalities, such as images (Lu et al., 2023; Yang et al., 2023). Although there have been significant advancements in the development of MLLMs for various tasks (Chen et al., 2023; Wu et al., 2023), none have specifically focused on lightweight models for the medical domain.
Local deployment is critical as many hospitals are concerned that uploading images to the cloud for AI processing may violate privacy laws such as the General Data Protection Regulation (GDPR) in Europe or the Personal Data Protection Act (PDPA) in Thailand. Addressing this issue is essential to ensure that patients can receive enhanced medical services while maintaining their privacy. Additionally, most hospitals in developing countries are GPUconstrained and lack access to high-end GPUs which are typically required for deployment. Therefore, it is imperative to develop lightweight models capable of performing inference on-premise using consumer-grade GPUs.
Motivated by these challenges, we investigate various architectures with a focus on identifying models that offer the optimal cost-to-performance ratio for local deployment. For the purposes of this study, we concentrate on the task of findings generation.
Our contributions are summarized as follows: • We developed and trained a lightweight Mul-
timodal Large Language Model (MLLM) for the radiology report generation task using a two-stage training strategy, achieving performance metrics comparable to those of larger models. • We introduced a novel two-stage postprocessing strategy. The first stage enhances the readability and clarity of the reports. The second stage, "First, Do No Harm" SafetyNet, employs the X-Raydar classification model to cross-verify the model outputs, significantly improving diagnostic accuracy and ensuring
patient safety.

heading: 2 Methodology

heading: 2.1 Model Architecture
text: Impressed by its superior performance, which surpasses even some larger models despite its lightweight nature in general domain, we decided to follow model architecture design of Bunny for this study (He et al., 2024). Our model components include the SigLIP-so400m2 (Zhai et al., 2023) as the visual encoder, a two-layer Multi-layer perceptron (MLP) with a GELU activation as the visionlanguage connector, and the Phi-2 2.7B as our LLM (Hughes, 2023).
The SigLIP visual encoder extracts meaningful features from chest X-ray images, enabling the model to capture relevant visual information. The MLP integrates these visual features with language representations. Phi-2, a 2.7 billion parameter lightweight language model trained on highquality data, achieves performance metrics comparable to substantially larger models. It demonstrates exceptional proficiency in benchmarks such as commonsense reasoning, language comprehension, question-answering, and coding tasks, frequently surpassing models with significantly more parameters.

heading: 2.2 Training Strategy & Datasets
text: We employ a two-stage training strategy to optimize our model’s performance. In the first stage, we train only the MLP connector using the LLaVaMed alignment 500k dataset (Li et al., 2023; Zhang et al., 2023), while keeping the rest of the model frozen. LLaVa-Med is a large-scale dataset specifically curated for medical vision-language tasks, containing a diverse collection of medical imaging modalities and tasks. By pretraining on this dataset, the MLP connector learns to effectively map visual features to language representations in the medical domain.
The second stage involves fine-tuning both the vision-language connector and the Language Model (LLM), while keeping the visual encoder frozen. This fine-tuning process utilizes the interpret-cxr dataset (Xu et al., 2024) comprising a mixture of multiple chest X-ray datasets: CheXpert (Chambon et al., 2024), PadChest (Bustos et al., 2020), BIMCV COVID-19 (Vayá et al., 2020), and MIMIC-CXR-JPG (Johnson et al., 2019). This
2SigLIP HuggingFace Link
dataset includes chest X-ray images along with their corresponding radiology reports, providing task-specific training data. For our study, we combined both findings and impressions into a single dataset, totaling 710,807 image-text pairs. In our preliminary study, retaining only the first image from each study outperformed using all images, as shown in 3. Therefore, we heuristically kept only the first image to preserve report diversity.

heading: 2.3 Two-Stage Post-Processing Strategy
text: In addition to our model’s architecture and training strategies, we implement a crucial post-processing strategy, wherein the model outputs undergo sequential processing to enhance the overall quality of the reports (See Appendix C for detailed prompts).

heading: 2.3.1 First stage: Report Refinement
text: In the first stage, we utilize a Large Language Model (LLM) to enhance the comprehensiveness of the findings reports. Our key objectives are to improve readability and clarity, eliminate nonsensical words, and remove duplicated sentences from the model hallucinations. For normal chest X-ray (CXR) findings, we provide detailed, standardized explanations to clarify the condition. We use a recommended vocabulary list to maintain consistency across reports. Our methodology promotes concise reporting by focusing on critical findings, while still adhering to a professional radiology report format. This includes transforming simple statements like "No significant findings" into comprehensive and detailed descriptions.

heading: 2.3.2 Second stage: "First, Do No Harm" SafetyNet
text: This post-processing strategy, termed "First, Do No Harm" SafetyNet, involves using an advanced X-ray classification model, X-Raydar, to provide a second opinion on chest X-ray images. This methodology mirrors the practice of doctors consulting with colleagues to validate the diagnoses, thereby mitigating the risk of errors that could potentially harm patients.
X-Raydar Integration X-Raydar, a state-of-theart X-ray classification model, is trained on a substantial dataset of 1.8 million chest X-rays, covering a wide range of pathologies (Cid et al., 2024). By integrating X-Raydar into our post-processing strategy, we leverage its robust performance to cross-verify and refine the outputs generated by
our MLLM. Second Opinion Inference A major challenge in findings generation is the occurrence of false negative errors, such as incorrectly reporting "lungs are clear" or "no cardiomegaly" To mitigate this issue, we use Llama3 70B3 with a specially designed prompt to detect and correct such critical errors. The prompt incorporates the classification results from X-Raydar to specifically address common false negative errors. For example, if X-Raydar identifies signs of cardiomegaly but the initial report states "no cardiomegaly," our tailored prompt for Llama3 ensures that the final report accurately reflects the patient’s condition. This dual-check strategy significantly increases agreement with the ground truth report, thereby improving diagnostic accuracy and enhancing patient safety.

heading: 3 Experimental Setup

heading: 3.1 Evaluation
text: We evaluated our approach using metrics for natural language generation (NLG) quality and clinical accuracy, as implemented by the Vilmedic framework (Delbrouck et al., 2022b).
NLG Metrics • BLEU measures the precision of n-grams in
the generated text compared to a reference text (Papineni et al., 2002). • ROUGE-L focuses on the longest common subsequence between the generated and reference texts (Lin, 2004). • BERTscore uses contextual embeddings to compare semantic similarity between the generated and reference texts (Zhang et al., 2019).
Clinical Accuracy Metrics • F1-CheXbert computes the F1 score based
on the similarity of indicator vectors for 14 pathologies (Smit et al., 2020). • F1-RadGraph calculates the overlap in clinical entities and relations extracted from the reports (Delbrouck et al., 2022a). These metrics provide a comprehensive evaluation of our model’s performance in generating accurate and clinically relevant radiology reports.

heading: 3.2 Model Architecture Ablations
text: To investigate the complex relationship between model architecture and overall performance across
3LLaMa3 70B Instruct HuggingFace Link
various metrics and tasks, we designed and conducted the following series of experiments to isolate specific architectural elements and their effects:
• Language Model: – Phi-2 2.7B – Llama3-OpenBioLLM 8B4 • Visual Encoder: – SigLIP – SigLIP with S2-Wrapper
In addition to our base model, Phi-2 2.7B with the SigLIP visual encoder, we conducted further ablation studies to understand the impact of different model architectures. For the language model (LLM), we selected Llama3-OpenBioLLM 8B as our larger model to investigate whether initializing from a medical LLM could enhance the performance of a MLLM on findings generation task. The model was fine-tuned using a comprehensive dataset of high-quality biomedical data, allowing it to comprehend and generate text with precise domain-specific accuracy and fluency. The Llama3OpenBioLLM 8B demonstrated exceptional performance on multiple medical LLM benchmarks5, surpassing even some larger models.
For the visual encoder, we employed the S2Wrapper, an extension designed to extract multiscale features from images (Shi et al., 2024). This approach was chosen to evaluate the impact of multi-scale feature extraction on the findings generation task. The integration of the S2-Wrapper aims to enhance the model’s ability to handle complex visual features and improve the overall accuracy of the generated reports.

heading: 4 Results & Discussion

heading: 4.1 Model Architecture Ablations
text: Our best architecture, Phi-2 combined with SigLIP visual encoder, demonstrates superior performance as indicated by the F1-Radgraph metric as presented in Table 1. Notably, this configuration
4LLaMa3 OpenBioLLM 8B HuggingFace Link 5OpenLLM Leaderboard
outperforms the S2-wrapper extension. We hypothesize that the general domain SigLIP visual encoder encounters difficulties in effectively extracting useful information from X-ray images at multiple scales. Additionally, this architecture surpasses the performance of the larger medical domain Llama3-OpenBioLLM 8B, suggesting that the success in this specific findings generation task may be more dependent on the quality of image information extracted by the visual encoder rather than the pretrained knowledge of LLMs.

heading: 4.2 Post-processing
text: Our two-stage post-processing strategy markedly improves the performance metrics for our findings generation task, as demonstrated by the hiddenfindings test results in Table 2. In the first stage, report refinement increased the F1-Radgraph metric from 22.61 to 23.11 (+0.5). The incorporation of the "First, Do No Harm" SafetyNet in the second stage further elevated the F1-Radgraph metric from 23.11 to 24.62 (+1.51), resulting in a total improvement of 2.01 points over the default model. This comprehensive approach not only enhances report readability but also significantly boosts diagnostic accuracy and patient safety, leading to higher quality radiology reports.

heading: 5 Conclusion
text: We present our approach to the Radiology Report Generation task in the BioNLP 2024 shared task. This study investigates various training configurations and data mixtures to develop lightweight models for generating radiology reports from chest X-ray images. Our findings demonstrate that even a smaller model, such as the Phi-2 language model, can perform comparably to larger models in the
report generation task. Additionally, incorporating post-processing techniques significantly enhances the quality of the reports and ensures patient safety. This is particularly crucial for hospitals in resourceconstrained settings. By focusing on models that can be fine-tuned on a single A100 GPU and operated on-premises with a consumer-grade GPU, we address privacy concerns and improve the accessibility of this technology.
Limitations
In this work, we utilized the Llama3-70b-instruct model on HuggingChat for post-processing in both stages, demonstrating that it improves the metric (F1-RadGraph) of the generated reports. However, we did not explicitly analyze the quality of postprocessing with smaller LLMs to determine if they can achieve similar results. Future research could explore post-processing with multiple LLM sizes to understand the impact of model size on performance. Additionally, our current approach involves sequential two-stage post-processing, which may not fully leverage the LLM’s capabilities and could introduce unnecessary complexity and latency. Combining these stages into a single step could reduce latency and streamline the overall process.

heading: Acknowledgments
text: This research is supported by PreceptorAI team and CARIVA Thailand. The experiments were conducted with the resources and services provided by the Siriraj Radiology Department (SIRAD). Their resources and domain expertise were instrumental in the successful completion of this study.

heading: A Preliminary Study
text: To investigate the impact of data composition and transfer learning on model performance, we conducted the following experiments:
A.1 Initialization and Training Strategies:
We explored two initialization and training strategies:
• Initialization from MLLM Pretrained on General Domain: This approach involves continually fine-tuning the pretrained multimodal language model (MLLM) on the interpret-cxr dataset, focusing solely on the final stage of training (stage 2). • Initialization from LLM and Randomly Initialized Adapter: In this method, the language model (LLM) is pretrained on the LLaVaMED dataset (stage 1) with a randomly initialized adapter, followed by fine-tuning on the interpret-cxr dataset (stage 2). This two-stage process aims to leverage domain-specific pretraining to enhance performance.
Our results indicated that this two-stage approach, which leverages domain-specific knowledge from LLaVa-MED, is beneficial and enhances performance.
A.2 Image Selection
We examined the effect of different image selection techniques:
• Reusing the Same Report When Multiple Images Are Provided: This technique involves using all available images for a given report, resulting in 1 million image-text pairs. This approach aims to maximize the amount of visual information provided to the model. • Using Only the First X-ray Image When Multiple Images Are Provided: Here, only the first image from each study is used, leading to a dataset of 700,000 image-text pairs. This method is intended to reduce redundancy and potential bias in the reports by focusing on the most relevant image.
Our data mixture study revealed that using only the first image from each study yielded slightly better performance than using all images, ensuring the diversity of the radiology reports.

heading: B Dataset Cleaning
text: In the preliminary inspection of the dataset, we observed that numerous reports within the interpret-
cxr dataset contained sentences with information that could not be derived solely from the X-ray images. These sentences included details such as dates, doctor information, references to other imaging modalities, and comparisons with previous findings. Such extraneous information introduces noise that may lead the model to hallucinate incorrect dates, numbers, and comparisons with non-existent prior studies (Chen et al., 2024).
To mitigate this issue, we attempted to utilize GPT-3.5 Turbo to remove this irrelevant information from the dataset. The dataset cleaning prompts and examples are detailed in Appendix B.1 and B.2. However, during the evaluation, we observed a slight decline in performance metrics, as illustrated in Table 4, following the removal of these sentences. We suspected that the public-test and hidden-test datasets did not undergo similar cleaning procedures, resulting in uncleaned test sets. Therefore, to maximize of our performance metrics, we decided to use the original dataset without data cleaning for the remaining of our study.
B.1 Cleaning Prompt We provide the prompt used for preprocessing and cleaning the training dataset to remove information that cannot be obtained solely from X-ray images. Findings: "Remove non-x-rays discernible information from chest x-ray findings i.e. date, previous report mentions and comparison, and information from other imaging modality. Keep all remaining sentences unchanged:" Impression: "Remove non-x-rays discernible information from chest x-ray impression i.e. date, doctor information, previous report mentions and comparison, and information from other imaging modality. Keep all remaining sentences unchanged. But if there is nothing left, return |None| and stop generating:"
B.2 Examples B.2.1 Comparison with previous report Original: Compared with the previous one, the x-ray is slightly inspired. no lung consolidations or pleural effusion are observed. Clean: No lung consolidations or pleural effusion are observed.
B.2.2 Date Mentions Original: AP chest radiograph on 12/11/08 at 2315 demonstrates a dual lead AICD. Stable cardiomegaly and stable left basilar opacities, likely
atelectasis. Persistent right-sided pleural effusion. Diffuse reticular opacities, mild interstitial edema. Elevation of the left hemidiaphragm. Hiatal hernia. Partially visualized abdominal aortic stent graft. AP chest radiograph on 12-11-2008 at 3:11 a.m. demonstrates no significant interval change in cardiopulmonary status. Clean: AP chest radiograph demonstrates a dual lead AICD. Stable cardiomegaly and stable left basilar opacities, likely atelectasis. Persistent rightsided pleural effusion. Diffuse reticular opacities, mild interstitial edema. Elevation of the left hemidiaphragm. Hiatal hernia. Demonstrates no significant interval change in cardiopulmonary status.
B.2.3 Other Modalities Mentions Original: Chest x-ray. bilateral bronchiectasis with a predominance on the right side, noting an increase in density around these right basal bronchiectasis in relation to consolidations described in previous ct. there is no pleural effusion. cardiomedastinal silhouette and hila are within normal limits. biapical caps. bone and soft parts without notable findings. Clean: Chest x-ray. Bilateral bronchiectasis with a predominance on the right side. There is no pleural effusion. Cardiomedastinal silhouette and hila are within normal limits. Biapical caps. Bone and soft parts without notable findings.
B.2.4 Doctor information Original: 1.Interval development and resolution of a right upper lobe opacification, possibly representing interval resolution of right upper lobe aspiration or asymmetric pulmonary edema. 2. Persistent small bilateral pleural effusions. ""Physi-
cian to Physician Radiology Consult Line: (753) 619-1110"" I have personally reviewed the images for this examination and agreed with the report transcribed above. Clean: 1.Interval development and resolution of a right upper lobe opacification, possibly representing interval resolution of right upper lobe aspiration or asymmetric pulmonary edema. 2. Persistent small bilateral pleural effusions."

heading: C LLM Prompts
text: We provide a template of our post-processing prompt for the LLM to enhance diverse aspects generated report. The Report Refinement prompt enhance the readability and clarify the report while the "First, Do No Harm" SafetyNet prompt of Llama3 combines the results of our MLLM model and the classification results from X-Raydar.
Prompt 1: First Stage: Report Refinement
Radiology Reporting Instructions You are an experienced radiologist tasked with interpreting CXR images and generating reports from free-text descriptions. Your primary objectives are to:
• Enhance readability and clarity of the text.
• Conduct Radgraph sterilization to ensure data integrity and accuracy.
When processing normal CXR findings, provide detailed explanations to clarify the condition. For instance:
Input Examples • No significant findings.
• No acute cardiopulmonary findings.
• No acute cardiopulmonary abnormality.
• The heart is normal in size. The mediastinum is unremarkable. The lungs are clear.
• The heart size and pulmonary vascularity appear within normal limits. The lungs are free of focal airspace disease. No pleural effusion or pneumothorax is seen.
• No acute cardiopulmonary findings.
Expected Output • The lungs are clear. No cardiomegaly. The cardiomediastinal and hilar contours are normal. There is no focal
consolidation, pleural effusion, or pneumothorax. The pulmonary vascular markings are normal. No free air beneath the diaphragm.
Use a recommended vocabulary list to standardize report language and maintain consistency across reports. This list includes [’AC’, ’Bony’, ’Borderline’, ’CHF’, ’Calcified’, ’Cardiac’, ’Cardiomediastinal’, ’Cardiomegaly’, ’Clips’, ’Dense’, ’Dobbhoff’, ’Esophageal’, ’Extensive’, ’Heart’, ’Healing’, ’Hilar’, ’Hyperinflated’, ’IJ’, ’Increase’, ’Increased’, ’Interval’, ’Interposition’, ’Lung’, ’Lungs’, ’Lucency’, ’Minimal’, ’Moderate’, ’Mild’, ’Mildly’, ’Monitoring’, ’Multiple’, ’Nasogastric’, ’Nearly’, ’New’, ’Normal’, ’Orphaned’, ’PICC’, ’Pneumomediastinum’, ’Pneumothorax’, ’Port - A - Cath’, ’Pulmonary’, ’Right-sided’, ’Small’, ’Slight’, ’Slightly’, ’Stable’, ’Subcutaneous’, ’Tip’, ’Venous’, ’Widespread’, ’Worsening’, ’Zone’, ’accessory’, ’acute’, ’adenocarcinoma’, ’air’, ’air-filled’, ’airspace’, ’along’, ’angles’, ’anterior’, ’anteriorly’, ’apparent’, ’appearance’, ’appropriately’, ’area’, ’areation’, ’artifact’, ’atelectasis’, ’axilla’, ’benign’, ’bibasal’, ’bilaterally’, ’blunting’, ’borderline’, ’bowel’, ’bronchovascular’, ’caliber’, ’calcification’, ’calcified’, ’cancer’, ’cardiac’, ’cardiomegaly’, ’central’, ’change’, ’chest’, ’chf’, ’clavicle’, ’clavicular’, ’clear’, ’clips’] Reports should be styled succinctly, focusing on critical findings and summarizing significant observations without omitting essential details. Each report should follow the professional radiology report format:
Example of Good Reports • The lungs are clear without focal consolidation. No pleural effusion or pneumothorax is seen. Heart size is
top-normal. The mediastinal silhouette is unremarkable.
• Portable frontal radiograph of the chest demonstrates a right chest tube in unchanged position ending at the right apex. The right basilar pneumothorax continues to decrease in size. The pneumomediastinum is also decreasing. Extensive subcutaneous emphysema persists. Stable heart size and mediastinal contours. Small left pleural effusion is unchanged.
• The cardiac, mediastinal and hilar contours appear stable. Streaky left basilar opacity suggests minor atelectasis. The lateral view depicts a greater degree of right middle lobe atelectasis than before, more coalescent. There is no definite pleural effusion or pneumothorax.
• Persistent hila with a congestive appearance possibly due to pulmonary edema, but without evidence of significant consolidations or pleural effusion. to be correlated clinically.
• Cardiac silhouette is unchanged. Aortic arch calcification seen. Pulmonary vascularity is within normal limits. There is trace right pleural effusion noted. Bibasilar atelectasis is seen. There is no pneumothorax. Multilevel degenerative changes seen in the thoracic spine.
(answer only summarize report to text paragraph)
Prompt 2: "Second Stage: First, Do No Harm" SafetyNet
Refine ’Input’ with ’Refine information’ indicating that the patient has conditions as refine information with the following conditions:
1. If the input and refined information have mismatched information, such as Refine information indicating an additional pathology not mentioned in the input, prioritize the refined information.
2. However, if the ’Refine information’ suggests a pathology already included in the ’Input’, we will not refine the input.
3. We will remove the sentence "lungs are clear" if there is any abnormality in the lung, pulmonary, or pleura.
Example 1: Input = The lungs are clear. No cardiomegaly. The cardiomediastinal and hilar contours are normal. There is no focal consolidation, pleural effusion, or pneumothorax. The pulmonary vascular markings are normal. Refine information = This patient has cardiomegaly and pleural effusion. Output should be = There is cardiomegaly. The cardiomediastinal and hilar contours are normal. There is pleural effusion. There is no focal consolidation or pneumothorax. The pulmonary vascular markings are normal. Example 2: Input = The lungs are clear. No cardiomegaly. The cardiomediastinal and hilar contours are normal. There is no focal consolidation, pleural effusion, or pneumothorax. The pulmonary vascular markings are normal. Refine information = This patient has cardiomegaly. Output should be = There is cardiomegaly. The lungs are clear. The cardiomediastinal and hilar contours are normal. There is no focal consolidation, pleural effusion, or pneumothorax. The pulmonary vascular markings are normal. Your answer should provide only the ’Output’ format and not include any other comments.

heading: Future Work
text: Future research could explore post-processing with multiple LLM sizes to understand the impact of model size on performance. Additionally, our current approach involves sequential two-stage post-processing, which may not fully leverage the LLM’s capabilities and could introduce unnecessary complexity and latency. Combining these stages into a single step could reduce latency and streamline the overall process