LLMs in Healthcare and Political Analysis: Quantifying Uncertainty and Predictive Innovations

The recent advancements in the application of Large Language Models (LLMs) across various domains, particularly in healthcare and political analysis, are significantly reshaping the landscape of research and practical applications. In healthcare, LLMs are being leveraged to enhance diagnostic accuracy, improve clinical documentation, and automate data harmonization processes. Notably, there is a growing focus on quantifying uncertainty in LLM predictions to ensure reliability in high-stakes applications such as clinical outcome predictions. This emphasis on uncertainty quantification is crucial for building trust and transparency in AI-driven healthcare solutions.

In the political domain, LLMs are being used to analyze and predict election outcomes, as well as to mediate political discourse through generative memes. The ability of LLMs to interpret complex human behavior and temporal dynamics is being explored to enhance predictive models for political events. Additionally, the use of LLMs in generating synthetic visuals and memes is changing how political information is disseminated and received on social media platforms.

Noteworthy developments include the successful application of LLMs in predicting pulmonary embolism phenotypes, the innovative use of distribution-based prediction for electoral results, and the advancement of Bayesian calibration methods for improving the reliability of LLM evaluators in text quality assessment. These innovations not only demonstrate the versatility of LLMs but also highlight the potential for further advancements in these fields.

Sources

MIMIC-IV-Ext-PE: Using a large language model to predict pulmonary embolism phenotype in the MIMIC-IV dataset

Generative Memesis: AI Mediates Political Memes in the 2024 USA Presidential Election

Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI

Evaluating the Impact of Lab Test Results on Large Language Models Generated Differential Diagnoses from Clinical Case Vignettes

A Natural Language Processing Approach to Support Biomedical Data Harmonization: Leveraging Large Language Models

Will Trump Win in 2024? Predicting the US Presidential Election via Multi-step Reasoning with Large Language Models

LLM Generated Distribution-Based Prediction of US Electoral Results, Part I

Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models

A Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients

Bayesian Calibration of Win Rate Estimation with LLM Evaluators

Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability

Built with on top of