Integrating AI and Generative Models in Healthcare

The recent advancements in the field of artificial intelligence and machine learning have seen significant developments in the application of large language models (LLMs) and generative models across various domains, particularly in healthcare. The integration of retrieval-augmented generation (RAG) with LLMs has shown promising results in enhancing the accuracy and reliability of clinical decision-making processes. This approach allows for the incorporation of domain-specific knowledge, thereby improving the performance of LLMs in tasks such as preoperative instruction generation and lung cancer staging. Additionally, the use of generative models for synthesizing high-fidelity 3D medical images from 2D sources has opened new avenues for more comprehensive diagnostic assessments, particularly in conditions like ARDS. The field is also witnessing innovations in data augmentation and synthesis techniques, which are crucial for training robust models under data-scarce conditions. These methods, including the use of diffusion models and conditional GANs, are not only enhancing the diversity and quality of training datasets but also enabling the creation of synthetic clinical trials, thereby addressing ethical and practical constraints in clinical research. Furthermore, advancements in multimodal learning, particularly in combining vision and language models, are being leveraged to develop versatile systems capable of handling complex medical tasks such as anatomical landmark localization and disease diagnosis. The integration of these technologies is poised to revolutionize medical imaging and diagnostic workflows, offering more efficient and accurate solutions for healthcare professionals.

Noteworthy papers include 'Retrieval-Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness,' which demonstrates the high accuracy of RAG models in preoperative healthcare tasks, and 'High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models,' which presents a novel approach to synthesizing high-quality 3D CT images for ARDS management.

Sources

oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness

High-Fidelity 3D Lung CT Synthesis in ARDS Swine Models Using Score-Based 3D Residual Diffusion Models

Generating Synthetic Datasets for Few-shot Prompt Tuning

Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging

SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data

Synthesizing Proton-Density Fat Fraction and $R_2^*$ from 2-point Dixon MRI with Generative Machine Learning

CtrlSynth: Controllable Image Text Synthesis for Data-Efficient Multimodal Learning

WeatherDG: LLM-assisted Procedural Weather Generation for Domain-Generalized Semantic Segmentation

Synthetic Augmentation for Anatomical Landmark Localization using DDPMs

Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation

Automatic Mapping of Anatomical Landmarks from Free-Text Using Large Language Models: Insights from Llama-2

VividMed: Vision Language Model with Versatile Visual Grounding for Medicine

MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models

A Survey on Data Synthesis and Augmentation for Large Language Models

MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models

Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis

Augmentation Policy Generation for Image Classification Using Large Language Models

Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?

Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning

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