Advances in AI for Healthcare and Medical Research

The field of artificial intelligence (AI) in healthcare and medical research is rapidly evolving, with a focus on developing innovative solutions to improve patient outcomes, enhance clinical decision-making, and accelerate medical discoveries. Recent studies have explored the application of large language models (LLMs) in various healthcare domains, including medical imaging, clinical text analysis, and patient engagement. Notably, LLMs have shown promise in tasks such as disease diagnosis, medical question answering, and clinical trial matching. Additionally, researchers have investigated the use of multimodal AI approaches, combining natural language processing with computer vision and other modalities, to analyze medical images and develop more accurate diagnostic tools. Furthermore, there is a growing interest in developing Explainable AI (XAI) methods to provide insights into AI-driven decision-making processes, ensuring transparency and trustworthiness in AI-based healthcare applications. Overall, the integration of AI in healthcare and medical research has the potential to revolutionize the field, enabling more efficient, effective, and personalized care.

Noteworthy papers in this area include DetoxAI, which introduces a Python toolkit for debiasing deep learning models in computer vision, and GaMNet, a hybrid network for efficient 3D glioma segmentation. Another notable study is the development of TrumorGPT, a graph-based retrieval-augmented large language model for fact-checking in the health domain. These innovative approaches demonstrate the significant advancements being made in AI for healthcare and medical research, highlighting the potential for AI-driven solutions to improve human health and wellbeing.

Sources

DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision

GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation

Summarisation of German Judgments in conjunction with a Class-based Evaluation

Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies

Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information

Enhancing BERTopic with Intermediate Layer Representations

Utilizing LLMs to Investigate the Disputed Role of Evidence in Electronic Cigarette Health Policy Formation in Australia and the UK

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration

CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection

Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI

Reassessing Large Language Model Boolean Query Generation for Systematic Reviews

HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling

Benchmarking Ethical and Safety Risks of Healthcare LLMs in China-Toward Systemic Governance under Healthy China 2030

Anatomical Attention Alignment representation for Radiology Report Generation

Development of a WAZOBIA-Named Entity Recognition System

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

DeltaEdit: Enhancing Sequential Editing in Large Language Models by Controlling Superimposed Noise

Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models

A Comparison Between Human and Generative AI Decision-Making Attributes in Complex Health Services

A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court

TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching

A Social Robot with Inner Speech for Dietary Guidance

LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs

NurValues: Real-World Nursing Values Evaluation for Large Language Models in Clinical Context

HealthBench: Evaluating Large Language Models Towards Improved Human Health

Performance Gains of LLMs With Humans in a World of LLMs Versus Humans

Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform

Tales of the 2025 Los Angeles Fire: Hotwash for Public Health Concerns in Reddit via LLM-Enhanced Topic Modeling

A Multimodal Multi-Agent Framework for Radiology Report Generation

Automated Detection of Clinical Entities in Lung and Breast Cancer Reports Using NLP Techniques

MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction

What Does Neuro Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs

On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging

The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine

From Questions to Clinical Recommendations: Large Language Models Driving Evidence-Based Clinical Decision Making

FactsR: A Safer Method for Producing High Quality Healthcare Documentation

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation

Large Language Models for Cancer Communication: Evaluating Linguistic Quality, Safety, and Accessibility in Generative AI