Navigating the Future of Healthcare DOI
Raaga Likhitha Musunuri, Ashruti Bhatt

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 73 - 106

Published: Aug. 16, 2024

This chapter explores the transformative potential of large language models (LLMs) and vision (LVMs) in healthcare. These technologies can comprehend generate human-like text interpret complex visual information, revolutionizing healthcare delivery. Applications include medical documentation, clinical decision support, imaging, patient education. The also addresses challenges like data bias, transparency, privacy, emphasizing robust frameworks interdisciplinary collaboration. enhance diagnostics, personalize treatments, optimize processes, improve efficiency, addressing global health disparities promoting equity.

Language: Английский

Progress and challenges in infectious disease surveillance and early warning DOI Creative Commons
Ying Shen, Youngjoon Hong, Thomas Krafft

et al.

Medicine Plus, Journal Year: 2025, Volume and Issue: unknown, P. 100071 - 100071

Published: Jan. 1, 2025

Language: Английский

Citations

3
Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining DOI Creative Commons

Emily Czekalski,

D.C. Watson

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 6, 2024

Abstract Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. Traditional methods requiring comprehensive retraining are resource-intensive and impractical for many applications. The proposed techniques knowledge injection, including integration adapter layers, retrieval-augmented generation (RAG), distillation, offer a novel significant solution to this challenge by enabling efficient updates without extensive retraining. Adapter layers allow specialized fine-tuning, preserving model's original capabilities while incorporating new information. RAG enhances contextual relevance generated responses dynamically retrieving pertinent information from base. Knowledge distillation transfers smaller larger model, augmenting its performance domains. Experimental results demonstrated substantial improvements accuracy, precision, recall, F1-score, along enhanced coherence. findings demonstrate potential maintain accuracy dynamic, information-rich environments, making them particularly useful fields timely accurate

Language: Английский

Citations

16
Generative AI in Pandemic Prediction DOI
Parisa Tavana,

Mojgan Zareinejad

Published: Jan. 1, 2025

Language: Английский

Citations

0
Navigating the Future of Healthcare DOI
Raaga Likhitha Musunuri, Ashruti Bhatt

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 73 - 106

Published: Aug. 16, 2024

This chapter explores the transformative potential of large language models (LLMs) and vision (LVMs) in healthcare. These technologies can comprehend generate human-like text interpret complex visual information, revolutionizing healthcare delivery. Applications include medical documentation, clinical decision support, imaging, patient education. The also addresses challenges like data bias, transparency, privacy, emphasizing robust frameworks interdisciplinary collaboration. enhance diagnostics, personalize treatments, optimize processes, improve efficiency, addressing global health disparities promoting equity.

Language: Английский

Citations

0