MediTools -- Medical Education Powered by LLMs

• URL: http://arxiv.org/abs/2503.22769v1
• Date: Fri, 28 Mar 2025 03:57:32 GMT
• Title: MediTools -- Medical Education Powered by LLMs
• Authors: Amr Alshatnawi, Remi Sampaleanu, David Liebovitz,
• Abstract summary: This research project leverages large language models to enhance medical education and address workflow challenges.<n>Our first tool is a dermatology case simulation tool that uses real patient images depicting various dermatological conditions.<n>The application also features two additional tools: an AI-enhanced tool for engaging with LLMs to gain deeper insights into research papers, and a Google News tool that offers LLM generated summaries of articles for various medical specialties.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) has been advancing rapidly and with the advent of large language models (LLMs) in late 2022, numerous opportunities have emerged for adopting this technology across various domains, including medicine. These innovations hold immense potential to revolutionize and modernize medical education. Our research project leverages large language models to enhance medical education and address workflow challenges through the development of MediTools - AI Medical Education. This prototype application focuses on developing interactive tools that simulate real-life clinical scenarios, provide access to medical literature, and keep users updated with the latest medical news. Our first tool is a dermatology case simulation tool that uses real patient images depicting various dermatological conditions and enables interaction with LLMs acting as virtual patients. This platform allows users to practice their diagnostic skills and enhance their clinical decision-making abilities. The application also features two additional tools: an AI-enhanced PubMed tool for engaging with LLMs to gain deeper insights into research papers, and a Google News tool that offers LLM generated summaries of articles for various medical specialties. A comprehensive survey has been conducted among medical professionals and students to gather initial feedback on the effectiveness and user satisfaction of MediTools, providing insights for further development and refinement of the application. This research demonstrates the potential of AI-driven tools in transforming and revolutionizing medical education, offering a scalable and interactive platform for continuous learning and skill development.

Related papers

• A Survey of Medical Vision-and-Language Applications and Their Techniques [48.268198631277315]
  Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-19T03:27:05Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges [33.20745682286796]
  The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry.<n>This perspective paper aims to discuss the inner workings of building LLM-powered medical AI applications.
  arXiv  Detail & Related papers  (2024-10-28T22:30:06Z)
• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• Large Language Models for Medicine: A Survey [31.720633684205424]
  Large language models (LLMs) have been developed to address challenges in the digital economy's landscape of digital intelligence. This paper focuses on the requirements and applications of medical LLMs.
  arXiv  Detail & Related papers  (2024-05-20T02:32:26Z)
• Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents [19.721008909326024]
  Large language models (LLMs) have sparked a new wave of technological revolution in medical artificial intelligence (AI)<n>We introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness.<n>Within the simulacrum, doctor agents are able to evolve by treating a large number of patient agents without the need to label training data manually.
  arXiv  Detail & Related papers  (2024-05-05T14:53:51Z)
• 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)
• Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review [16.008511195589925]
  Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-03T13:51:36Z)
• Redefining Digital Health Interfaces with Large Language Models [69.02059202720073]
  Large Language Models (LLMs) have emerged as general-purpose models with the ability to process complex information. We show how LLMs can provide a novel interface between clinicians and digital technologies. We develop a new prognostic tool using automated machine learning.
  arXiv  Detail & Related papers  (2023-10-05T14:18:40Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)

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.