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Related Experiment Video

Updated: May 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Generative Artificial Intelligence in Medicine.

Divya Shanmugam¹, Monica Agrawal^2,3, Rajiv Movva¹

¹Department of Computer Science, Cornell Tech, New York, NY, USA.

Annual Review of Biomedical Data Science

|March 18, 2025

Summary

This summary is machine-generated.

Generative artificial intelligence (AI) offers new medical applications for various healthcare professionals and patients. Realizing AI's potential requires addressing key challenges in privacy, transparency, equity, and model evaluation.

Keywords:

generative AI healthcare large language models medicine

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Area of Science:

Medical Informatics
Artificial Intelligence
Healthcare Technology

Background:

Generative artificial intelligence (AI) capabilities are rapidly advancing.
These advancements present numerous potential applications within the medical field.

Purpose of the Study:

To provide a comprehensive overview of generative AI use cases in medicine.
To identify and discuss the challenges hindering the adoption of generative AI in healthcare.
To highlight open research directions for generative AI in medicine.

Main Methods:

Literature review and synthesis of current generative AI applications in medicine.
Analysis of challenges related to privacy, security, transparency, interpretability, equity, and model evaluation.
Identification of future research avenues.

Main Results:

Generative AI has diverse applications for clinicians, patients, trial organizers, researchers, and trainees.
Significant challenges exist, including data privacy, model transparency, equitable access, and rigorous validation.
Numerous research opportunities are present to overcome these hurdles.

Conclusions:

Generative AI holds transformative potential for healthcare delivery and medical research.
Addressing ethical, technical, and practical challenges is crucial for successful implementation.
Continued research is essential to unlock the full benefits of generative AI in medicine.