Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

• URL: http://arxiv.org/abs/2406.12449v1
• Date: Tue, 18 Jun 2024 09:53:37 GMT
• Title: Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
• Authors: Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle S Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu,
• Abstract summary: Retrieval-augmented generation (RAG) enables models to generate more accurate contents by leveraging the retrieval of external knowledge. RAG can pave the way for connecting generative AI with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.
• Score: 10.004952611099947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.

Related papers

• Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report [12.204470166456561]
  Generative AI shows significant potential in health economics and outcomes research (HEOR) Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration.
  arXiv  Detail & Related papers  (2024-10-26T15:42:50Z)
• The Potential and Perils of Generative Artificial Intelligence for Quality Improvement and Patient Safety [27.753117791280857]
  Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance.
  arXiv  Detail & Related papers  (2024-06-23T15:01:11Z)
• Rapid Review of Generative AI in Smart Medical Applications [3.068678059223457]
  Generative models, a key AI technology, have revolutionized medical image generation, data analysis, and diagnosis. This article explores their application in intelligent medical devices. Generative models show great promise in medical image generation, data analysis, and diagnosis.
  arXiv  Detail & Related papers  (2024-06-08T03:34:47Z)
• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis [17.4235794108467]
  The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes.
  arXiv  Detail & Related papers  (2024-03-26T09:55:49Z)
• Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness [47.51360338851017]
  Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task.
  arXiv  Detail & Related papers  (2023-11-19T03:29:45Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC [54.74071593520785]
  Mobile AIGC can be a key enabling technology for an emerging application, called human digital twin (HDT) HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services.
  arXiv  Detail & Related papers  (2023-07-22T15:59:03Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.