Large Language Models (LLMs) in Biomedicine and Healthcare

Report on Current Developments in the Research Area of Large Language Models (LLMs) in Biomedicine and Healthcare

General Direction of the Field

The field of large language models (LLMs) in biomedicine and healthcare is rapidly evolving, with a strong emphasis on enhancing reliability, accuracy, and fairness in AI-driven applications. Recent advancements are primarily focused on mitigating hallucinations in LLMs, particularly in high-stakes domains like medical question answering and disease diagnosis. Techniques such as Retrieval-Augmented Generation (RAG), iterative feedback loops, supervised fine-tuning, and prompt engineering are being adapted and optimized for the medical domain, where strict adherence to medical guidelines and specialized knowledge are paramount.

Another significant trend is the integration of LLMs into epidemic surveillance and disease outbreak forecasting. These models are being leveraged to extract valuable information from unstructured data sources, enhancing the accuracy and timeliness of epidemic modeling and forecasting. The use of multilateral attention-enhanced deep neural networks is also gaining traction, offering improved forecasting performance by capturing complex relationships and temporal dependencies in the data.

In the realm of medical diagnostics, LLMs are being increasingly utilized for automatic disease diagnosis. However, there is a growing recognition of the need for comprehensive evaluation methods and data preprocessing strategies to ensure the models' reliability and fairness. The field is also grappling with issues related to data privacy, model interpretability, and ethical implications, particularly in the context of sensitive biomedical data.

Noteworthy Innovations

  1. Mitigating Hallucinations in Medical LLMs: Techniques like RAG and iterative feedback loops are being refined for medical contexts, enhancing the reliability of AI systems in clinical decision-making.

  2. Epidemic Surveillance with LLMs: The use of LLMs for epidemic information extraction and forecasting is proving to be a promising tool for managing future pandemic events.

  3. Fairness in Medical Diagnostics: Research is exploring methods to evaluate and mitigate bias in LLMs, ensuring equitable performance across diverse populations.

  4. Comprehensive Surveys on LLMs in Biomedicine: Recent surveys provide a holistic view of LLMs' capabilities and challenges in biomedicine, guiding future research directions.

These developments underscore the transformative potential of LLMs in biomedicine and healthcare, while also highlighting the critical need for ongoing research to address the field's unique challenges.

Sources

Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models

Epidemic Information Extraction for Event-Based Surveillance using Large Language Models

Classification of Safety Events at Nuclear Sites using Large Language Models

Evaluating ChatGPT on Nuclear Domain-Specific Data

A Multilateral Attention-enhanced Deep Neural Network for Disease Outbreak Forecasting: A Case Study on COVID-19

Large Language Models for Disease Diagnosis: A Scoping Review

Evaluating Pre-Training Bias on Severe Acute Respiratory Syndrome Dataset

Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data

Does Data-Efficient Generalization Exacerbate Bias in Foundation Models?

A Survey for Large Language Models in Biomedicine

Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine