This study introduces a transformative framework for medical education by
integrating semi-structured data with Large Language Models (LLMs), primarily
OpenAIs ChatGPT3.5, to automate the creation of medical simulation scenarios.
Traditionally, developing these scenarios was a time-intensive process with
limited flexibility to meet diverse educational needs. The proposed approach
utilizes AI to efficiently generate detailed, clinically relevant scenarios
that are tailored to specific educational objectives. This innovation has
significantly reduced the time and resources required for scenario development,
allowing for a broader variety of simulations. Preliminary feedback from
educators and learners has shown enhanced engagement and improved knowledge
acquisition, confirming the effectiveness of this AI-enhanced methodology in
simulation-based learning. The integration of structured data with LLMs not
only streamlines the creation process but also offers a scalable, dynamic
solution that could revolutionize medical training, highlighting the critical
role of AI in advancing educational outcomes and patient care standards.

采用半结构化数据与大型语言模型（LLMs）相结合，本研究引入了一种医学教育变革框架，以自动化生成医学模拟场景。该创新显著缩短了场景开发所需的时间和资源，实现了更广泛的模拟类型，提高了参与度和知识获取，突显了人工智能在推动教育成果和患者护理标准方面的关键作用。

通过半结构化数据和大型语言模型集成自动生成高质量的医学仿真场景

Automated Generation of High-Quality Medical Simulation Scenarios  Through Integration of Semi-Structured Data and Large Language Models

Realistic and diverse simulation scenarios with reactive and feasible agent
behaviors can be used for validation and verification of self-driving system
performance without relying on expensive and time-consuming real-world testing.
Existing simulators rely on heuristic-based behavior models for background
vehicles, which cannot capture the complex interactive behaviors in real-world
scenarios. To bridge the gap between simulation and the real world, we propose
TrajGen, a two-stage trajectory generation framework, which can capture more
realistic behaviors directly from human demonstration. In particular, TrajGen
consists of the multi-modal trajectory prediction stage and the reinforcement
learning based trajectory modification stage. In the first stage, we propose a
novel auxiliary RouteLoss for the trajectory prediction model to generate
multi-modal diverse trajectories in the drivable area. In the second stage,
reinforcement learning is used to track the predicted trajectories while
avoiding collisions, which can improve the feasibility of generated
trajectories. In addition, we develop a data-driven simulator I-Sim that can be
used to train reinforcement learning models in parallel based on naturalistic
driving data. The vehicle model in I-Sim can guarantee that the generated
trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give
comprehensive metrics to evaluate generated trajectories for simulation
scenarios, which shows that TrajGen outperforms either trajectory prediction or
inverse reinforcement learning in terms of fidelity, reactivity, feasibility,
and diversity.

本论文提出了一个用于自动驾驶性能验证的仿真器 TrajGen，该仿真器基于自然驾驶数据进行强化学习，可以直接从人类演示中捕捉更真实的行为，并生成可靠而多样的行动轨迹来评估仿真场景中的性能表现。

TrajGen: 为自动驾驶生成现实和多样化的轨迹，并包含反应性和可行性的代理行为

TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving

We present pyroomacoustics, a software package aimed at the rapid development
and testing of audio array processing algorithms. The content of the package
can be divided into three main components: an intuitive Python object-oriented
interface to quickly construct different simulation scenarios involving
multiple sound sources and microphones in 2D and 3D rooms; a fast C
implementation of the image source model for general polyhedral rooms to
efficiently generate room impulse responses and simulate the propagation
between sources and receivers; and finally, reference implementations of
popular algorithms for beamforming, direction finding, and adaptive filtering.
Together, they form a package with the potential to speed up the time to market
of new algorithms by significantly reducing the implementation overhead in the
performance evaluation step.

pyroomacoustics 是一个软件包，旨在快速开发和测试音频阵列处理算法。该软件包的内容可以分为三个主要组成部分：用于快速构建 2D 和 3D 房间内多个声源和麦克风的不同模拟场景的直观 Python 面向对象接口；用于一般多面房间的图像源模型的快速 C 实现，以有效地生成房间脉冲响应并模拟源和接收器之间的传播；最后，流行算法的参考实现，包括波束成型、方向发现和自适应滤波。三者共同组成一个软件包，可以显著减少实现开销，加速新算法上市的时间。