Generative Artificial Intelligence: Implications for Biomedical and Health Professions Education
Project Overview
Generative AI, especially large language models (LLMs), is transforming education in biomedical and health professions by facilitating various tasks such as answering clinical queries, excelling in assessments, and simulating clinical scenarios. While the technology showcases remarkable capabilities, concerns arise regarding its impact on students' knowledge retention and critical thinking skills. The document highlights the necessity of fostering AI competencies among both students and educators to maximize the advantages of generative AI while navigating challenges related to its accuracy, reliability, and ethical implications in educational settings. By addressing these issues, the integration of AI can enhance learning experiences and outcomes in health-related fields, ensuring that students not only benefit from advanced technology but also develop essential analytical skills.
Key Applications
Generative AI for content generation and feedback
Context: Biomedical and health professions students, healthcare professionals, students in programming courses, and educators across various disciplines requiring support in clinical reasoning, coding tasks, academic writing, and understanding complex topics.
Implementation: Integration of large language models (LLMs) and retrieval-augmented generation (RAG) systems to assist in educational assessments, simulations, coding tasks, and content summarization. This includes the use of AI tools to generate summaries of scientific articles, provide feedback on academic writing, and assist in answering clinical questions or programming challenges.
Outcomes: Enhanced student engagement and personalized learning through high-quality summaries and feedback, significant improvements in the accuracy of responses to clinical queries, and facilitated learning in coding tasks. However, challenges exist such as potential over-reliance on AI tools, accuracy issues, and the risk of not developing critical skills.
Challenges: Concerns about over-reliance on AI tools, potential dilution of critical thinking skills, accuracy of AI-generated content, complexity of ensuring model reliability, risk of hallucinated responses, and the potential for incorrect solutions affecting students' learning processes.
Implementation Barriers
Technological
Inconsistencies in the accuracy and reliability of AI-generated content, especially concerning hallucinations and factual errors. Concerns about bias in AI outputs, which can perpetuate existing inequalities in healthcare and education.
Proposed Solutions: Improving model training and incorporating retrieval-augmented generation techniques to enhance factual accuracy. Implementing robust frameworks for evaluating and mitigating bias in AI training datasets and outputs.
Educational Policy
Lack of comprehensive policies within educational institutions regarding the use of AI tools, leading to confusion about academic integrity.
Proposed Solutions: Development of clear guidelines on the use of generative AI in coursework and assessments to prevent academic misconduct.
Project Team
William Hersh
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: William Hersh
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai