Integrating Generative Artificial Intelligence Into Medical Education: Curriculum, Policy, and Governance Strategies.

The rapid advancement of generative artificial intelligence (GAI) is poised to revolutionize medical education, clinical decision-making, and health care workflow. Despite considerable interest and a surfeit of newly available tools, medical educators largely lack both competencies and guidance on how to incorporate the new and rapidly evolving world of GAI into the core medical school curriculum and experiences of undergraduate medical education. This Scholarly Perspective highlights the need for medical schools to adapt to this new paradigm by implementing policies, governance, and curricula that address the ethical, technical, and pedagogical implications of GAI.

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement.

Using Generative Artificial Intelligence in Medical Education.

Large Language Models (LLMs) are a type of generative artificial intelligence (AI) that produce realistic-sounding language in response to text prompts, giving AI the capability to simulate human discourse in various domains, including medical education.1 The pace of technological advancement is staggering, which comes with promise and peril. This Last Page summarizes some potential LLM uses in medical education.

Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine.

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship.

Co-design and evaluation of an audio podcast about sustainable development goals for undergraduate nursing and midwifery students.

Background: Sustainable Development Goals (SDGs) are universally recognised targets designed to combat poverty, inequality, and climate change. However, there exists limited awareness and understanding of these goals among nursing and midwifery students. To address this knowledge gap, a co-designed audio podcast was introduced as an educational tool to enhance students' comprehension of SDGs and their relevance to the healthcare profession.

Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial.

Importance: Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning.

Objective: To assess the effect of an LLM on physicians' diagnostic reasoning compared with conventional resources.

Design, Setting, And Participants: A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023.

Large Language Model Influence on Management Reasoning: A Randomized Controlled Trial.

Importance: Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown.

Objective: To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources.

Design: Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024.

Finding the right combination for self-directed learning: A focus group study of residents' choice and use of digital resources to support their learning.

Background: There has been a shift in postgraduate medical education towards digital educational resources-podcasts, videos, social media and other formats consumed asynchronously and apart from formal curricula. It is unclear what drives residents to select and use these resources. Understanding how and why residents choose digital resources can aid programme directors, faculty and residents in optimising residents' informal learning time.

Be in the Digital Room Where it Happens, Part II: Social Media for Neurology Educators.

Social media has changed the way we communicate and interact. Unsurprisingly, it has also changed how we teach and learn. Younger generations of learners have transitioned from traditional educational sources to digital ones.

Be in the Digital Room Where it Happens, Part I: Tweeting & Technology for Career Development.

Social media has become a part of everyday life. It has changed the way we obtain and distribute information, connect, and interact with others. As the number of platforms and users grow, medical professionals have learned the value social media can have in education, research, advocacy, and clinical care initiatives.

How podcasts teach: A comprehensive analysis of the didactic methods of the top hundred medical podcasts.

Purpose: Medical podcasts have grown in popularity, but little is known about their didactic methods. This study sought to systemically describe the pedagogical approach employed by the 100 most popular medical podcasts in the United States. This study also aimed to assess factors related to quality control and conflicts of interest in podcasting.