# Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models

arXiv: https://arxiv.org/abs/2409.02530 · HTML: https://mlsanity.com/paper/2409.02530/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health](https://mlsanity.com/paper/2306.10070/) (2023)
- [Visual Madlibs: Fill in the blank Image Generation and Question Answering](https://mlsanity.com/paper/1506.00278/) (2015)

## Built on by (most cited first, 1 of 1)

- [Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework](https://mlsanity.com/paper/2507.22464/) (2025)

## Abstract (arXiv)

The estimated Glomerular Filtration Rate (eGFR) is an essential indicator of kidney function in clinical practice. Although traditional equations and Machine Learning (ML) models using clinical and laboratory data can estimate eGFR, accurately predicting future eGFR levels remains a significant challenge for nephrologists and ML researchers. Recent advances demonstrate that Large Language Models (LLMs) and Large Multimodal Models (LMMs) can serve as robust foundation models for diverse applications. This study investigates the potential of LMMs to predict future eGFR levels with a dataset consisting of laboratory and clinical values from 50 patients. By integrating various prompting techniques and ensembles of LMMs, our findings suggest that these models, when combined with precise prompts and visual representations of eGFR trajectories, offer predictive performance comparable to existing ML models. This research extends the application of foundation models and suggests avenues for future studies to harness these models in addressing complex medical forecasting challenges.