Browse AI research papers authored by Hadas Ben Atya. Found 1 papers.

Reliable extraction of structured data from radiology reports using Large Language Models (LLMs) remains challenging, especially for complex, non-English texts like Hebrew. This study introduces an agent-based uncertainty-aware approach to improve the trustworthiness of LLM predictions in medical applications. We analyzed 9,683 Hebrew radiology reports from Crohn's disease patients (from 2010 to 2023) across three medical centers. A subset of 512 reports was manually annotated for six gastrointestinal organs and 15 pathological findings, while the remaining reports were automatically annotated using HSMP-BERT. Structured data extraction was performed using Llama 3.1 (Llama 3-8b-instruct) with Bayesian Prompt Ensembles (BayesPE), which employed six semantically equivalent prompts to estimate uncertainty. An Agent-Based Decision Model integrated multiple prompt outputs into five confidence levels for calibrated uncertainty and was compared against three entropy-based models. Performance was evaluated using accuracy, F1 score, precision, recall, and Cohen's Kappa before and after filtering high-uncertainty cases. The agent-based model outperformed the baseline across all metrics, achieving an F1 score of 0.3967, recall of 0.6437, and Cohen's Kappa of 0.3006. After filtering high-uncertainty cases (greater than or equal to 0.5), the F1 score improved to 0.4787, and Kappa increased to 0.4258. Uncertainty histograms demonstrated clear separation between correct and incorrect predictions, with the agent-based model providing the most well-calibrated uncertainty estimates. By incorporating uncertainty-aware prompt ensembles and an agent-based decision model, this approach enhances the performance and reliability of LLMs in structured data extraction from radiology reports, offering a more interpretable and trustworthy solution for high-stakes medical applications.

## Overview

* Research on extracting data from Hebrew radiology reports using large language models (LLMs)
* Developed uncertainty-aware approach with agent-based decision making
* Analyzed 9,683 reports from Crohn's disease patients across three medical centers
* Used Llama 3.1 model with Bayesian Prompt Ensembles
* Achieved improved accuracy after filtering uncertain predictions
* Demonstrated better reliability for medical applications

## Plain English Explanation

Getting useful information from medical reports written in languages like Hebrew is hard for AI systems. This research team developed a smarter way to do it by making the AI express how confident it is in its answers.

Think of it like having multiple doctors look at the same medical report. Each doctor might interpret things slightly differently. By combining their views and noting where they agree or disagree, you get a more reliable final opinion.

The [agent-based uncertainty awareness](https://aimodels.fyi/papers/arxiv/agent-based-uncertainty-awareness-improves-automated-radiology) system works like a chief doctor who looks at all these opinions and decides how confident they can be in the final diagnosis. When the system isn't sure, it says so instead of making risky guesses.

## Key Findings

The research showed impressive improvements in accuracy when using this new approach. The system performed best when it could admit uncertainty about difficult cases:

* F1 score improved from 0.3967 to 0.4787 after filtering uncertain predictions
* Achieved 64.37% recall on initial testing
* System could clearly separate between correct and incorrect predictions
* [Multi-agent diagnostic assistance](https://aimodels.fyi/papers/arxiv/magda-multi-agent-guideline-driven-diagnostic-assistance) showed better calibrated uncertainty estimates

## Technical Explanation

The team used **Llama 3.1** (8B parameter version) as their base model. They created six different ways to ask the same question (prompts) and used **Bayesian Prompt Ensembles** to estimate uncertainty.

The [language models retrieval](https://aimodels.fyi/papers/arxiv/language-models-retrieval-augmented-generation-automated-structured) system processed reports in multiple stages:

1. Initial processing of 9,683 Hebrew radiology reports
2. Manual annotation of 512 reports for validation
3. Automatic annotation of remaining reports using HSMP-BERT
4. Implementation of agent-based decision model with five confidence levels

## Critical Analysis

Several limitations deserve consideration:

* Limited to Hebrew language reports only
* Focused specifically on Crohn's disease cases
* Relatively small manually annotated dataset
* [Automated structured generation](https://aimodels.fyi/papers/arxiv/enhancing-llms-impression-generation-radiology-reports-through) may need further validation

The research could benefit from testing across more languages and medical conditions. Additionally, the impact of different prompt designs needs more investigation.

## Conclusion

This research represents a significant step forward in making AI systems more trustworthy for medical applications. The [agentic LLM workflows](https://aimodels.fyi/papers/arxiv/agentic-llm-workflows-generating-patient-friendly-medical) approach of combining multiple perspectives with uncertainty awareness shows promise for improving medical data extraction.

The ability to know when to express uncertainty rather than make potentially dangerous mistakes is crucial for healthcare applications. This work provides a foundation for developing more reliable AI systems in medicine.

Hadas Ben Atya

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