AGI在医学教育中的革命性应用：通用人工智能开启智能模拟与决策支持

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随着大型人工智能模型（Large AI Models，LAMs）的崛起，包括大规模语言模型、视觉模型以及多模态模型，通用人工智能（Artificial General Intelligence, AGI）正日益引起全球关注，被视为具有革命性影响的技术。AGI旨在在计算机系统内复制人类智能，使其能够在诸如推理、问题解决、决策制定以及理解和处理人类情感和社会互动等高要求任务上展现出接近人类的性能。在医学教育与培训领域，AGI的应用潜力巨大，能够革新传统的学习方式和实践训练。本研究论文深入探讨了AGI的基础概念、能力范围及其在医学教育中的具体应用。首先，作者澄清了AGI的核心定义，即它不仅局限于特定的专业技能，而是涵盖了广泛的认知功能，使其能够在模拟环境下模拟临床情境，提供实时反馈和指导。AGI通过预训练模型的强大能力，能够生成逼真的病人案例，辅助医生进行诊断决策，甚至模拟复杂的手术过程，从而提升医学生和实习医生的实践操作能力。医学模拟环境是AGI在医疗教育中的关键应用之一。这些模拟可以精确地模拟各种疾病发展和治疗反应，帮助医学生在安全的环境中练习诊断、干预和危机管理。此外，AGI还能通过自然语言处理技术解析海量的医学文献，为医学生提供即时的学习资源和解答疑惑，加速知识的吸收和理解。 AGI的另一个潜在优势在于个性化学习。通过分析个体的学习风格和进度，AGI可以提供定制化的教学路径，确保每个学生都能按照自己的节奏和需求获取最有效的教育资源。同时，AGI还可以监控和评估学生的进步，为教师提供及时的教学反馈，以便调整教学策略。然而，尽管AGI在医学教育中的潜力显著，也存在挑战和伦理考量。例如，如何确保AI系统的决策公正无偏，保护患者隐私，以及防止过度依赖AI导致医学生临床判断能力的退化。因此，研究者和政策制定者需要在推动AGI技术发展的同时，同步探讨相关的规范和道德框架。总结来说，这篇论文对用于医学教育的AGI进行了详尽的研究，揭示了其在提升医疗知识理解、技能训练和个性化学习方面的巨大价值。未来，随着技术的进步和伦理讨论的深入，AGI有望成为医学教育领域的基石，助力培养出更高效、精准的医疗人才。

eﬃcient parallelism to enhance performance. Importantly, Large Vision Models

extend their transformative capabilities to fundamental computer vision tasks

beyond classiﬁcation. A signiﬁcant breakthrough in the segmentation task has

been achieved with the Segment Anything (SAM) model (Kirillov et al., 2023).

SAM comprises a ViT-H image encoder, a prompt encoder, and a transformer-

based mask decoder, which predicts object masks. SAM’s remarkable zero-shot

generalization ability enables it to segment previously unseen objects and im-

ages. To train SAM, the construction of the largest segmentation dataset to

date, SA-1B, featuring over 1 billion masks, represents a notable milestone in

this ﬁeld.

Large multi-modal models, such as Large Vision-Language Models (LVLMs),

have shown remarkable success in various tasks, expanding their inﬂuence into

the realm of vision-language understanding (Zhe Gan et al, 2022). This success

has spawned a line of research dedicated to exploring the potential of LVLMs,

with a focus on both contrastive learning (Alec Radford et al, 2021, Xiyang Dai

et al, 2021, Chao Jia et al, 2021, Chunyuan L et al, 2022) and generative model-

ing (Danny Driess et al, 2023, Jean-Baptiste Alayrac et al, 2022, Jianfeng Wang

et al, 2022, and Liu et al, 2023). Remarkably, Liu et al, 2023, have demonstrated

that LVLMs exhibit exceptional zero-shot Optical Character Recognition (OCR)

performance without explicit training on OCR-speciﬁc data. This ﬁnding un-

derscores the critical importance of understanding the capabilities of LVLMs

in handling text-related visual tasks, considering their unique ability to extract

contextual information from various data sources, including text and images.

One noteworthy example of a generative pre-trained LVLM is GPT-4 (OpenAI,

2023), which has showcased exceptional visual comprehension and reasoning

abilities. While GPT-4 has achieved near-human performance on professional

and academic benchmarks, detailed technical speciﬁcations of the model remain

undisclosed. However, the primary focus of this discussion revolves around a

speciﬁc category of LVLMs: Large Vision Language Models (LVLMs) that ven-

ture beyond vision and language. Typically, LVLMs employ a dual-stream ar-

chitecture, where input text and images undergo separate encoding processes

to extract relevant features. For representation learning, the features from dif-

ferent modalities are either aligned through contrastive learning (A. Radford et

al, 2021 and Chen et al, 2021) or fused into a uniﬁed representation using an

additional encoder (V. Goswami et al., 2022 and Wang et al., 2023). The en-

tire model, encompassing both unimodal and multimodal encoders, undergoes

pre-training on large-scale image-text datasets and is subsequently ﬁne-tuned

for speciﬁc tasks or used for zero-shot tasks without further ﬁne-tuning. Pre-

training objectives may involve a combination of multi-modal and unimodal

tasks, with common multi-modal tasks encompassing image-text contrastive

learning, image-text matching, autoregressive modeling, masked modeling, and

image-grounded text generation.

Recent studies suggest that scaling up unimodal encoders and engaging in

multi-objective pre-training across both uni- and multi-modalities can signiﬁ-

cantly enhance multi-modal representation learning. LVLMs have recently made

substantial progress in text-to-image generation, employing two main method-

ologies: autoregressive models (Goh et al., 2021 and Nichol et al., 2022) and

diﬀusion models (Nichol et al., 2022 and Saharia et al., 2022). Autoregres-

sive models concatenate tokens from text and images to predict the next item

in a sequence, while diﬀusion models perturb images with random noise and

then progressively denoise them to restore the original image, with text descrip-

tions integrated into the process. It’s common to reuse predeﬁned LVLM and

LVLM architectures or their pre-trained parameters as encoders, with the scale

of these encoders signiﬁcantly inﬂuencing the quality of generation and lan-

guage understanding. Furthermore, Large Vision-Language Models (LVLMs)

have made substantial strides in text-to-image generation, image-grounded text

generation, and joint generation, primarily due to increased data, computa-

tional resources, and the number of model parameters (Nichol et al, 2021, Chen

et al, 2022, Suganthan et al, 2022). Notably, GPT-4 (OpenAI, 2023), after ﬁne-

tuning and human feedback alignment, demonstrates the capability to engage

in conversations with human users and supports visual inputs.

3 The Potential of AGI in Transforming Future

Medical Education and Training

Artiﬁcial General Intelligence (AGI) holds the promise of revolutionizing the

landscape of education by redeﬁning how teaching, learning, and assessment are

approached. AGI-driven educational systems can leverage their broad cognitive

abilities and adaptability to gain a deep understanding of individual students,

cater to their speciﬁc learning requirements, and craft personalized educational

experiences (Mohammed and ‘Nell’ Watson, 2019). This transformation is not

limited to traditional education, and the realm of medical education stands

to beneﬁt as well. Cutting-edge Language and Medical Models (LAMs), such

as GPT-4 (O, 2023) and Med PaLM 2 (K. Singhal, T. Tu, J. Gottweis et al,

2023), have demonstrated impressive performance by achieving scores of over

86% in the United States Medical Licensing Examination (USMLE). These mod-

els exhibit a robust knowledge spectrum and reasonable proﬁciency in areas like

bioethics, clinical reasoning, and medical management. The generative capabil-

ities of LAMs open up exciting opportunities for enhancing medical education.

These models can augment students’ learning experiences by providing addi-

tional insights through AI-generated content, as highlighted in (T. H. Kung,

M. Cheatham, and A. Medenilla et al, 2023). A well-informed and socially

adept LAM can act as a companion learning assistant, oﬀering prompt answers

to medical queries and simplifying complex medical terminology and practices.

For instance, the latest GPT-4 model (O, 2023) can serve as a Socratic tutor,

guiding students to discover answers independently, representing a pivotal step

in the practical adoption of LAMs in education, as their instructional methods

can be tailored to meet speciﬁc needs. Furthermore, LLMs, like ChatGPT (H.

Dai, Z. Liu, W. Liao et al, 2023), with their sentence paraphrasing abilities, can

assist students with dyslexia in their learning. However, legitimate concerns

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AGI在医学教育中的革命性应用：通用人工智能开启智能模拟与决策支持

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