Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.

Algorithms·2026
Same author

Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.

Algorithms·2026
Same author

Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.

JMIR formative research·2026
Same author

The effect of distributional information on the categorization of unaccusativity.

Journal of child language·2026
Same author

Correlates of Fitness Tracker Ownership and Use in Cancer Survivors: Cross-Sectional Survey.

JMIR cancer·2026
Same author

mHealth technologies in research studying cardiovascular health in cancer: A systematic review.

PLOS digital health·2025
Same journal

Supporting Radiology Resident Education and Clinical Decision-Making With Large Language Models: Comparative Study of Reasoning Models DeepSeek-R1 and ChatGPT-o1.

JMIR AI·2026
Same journal

Patient Perceptions on the Use of Artificial Intelligence in Creating Clinical Research Documents: Survey Study.

JMIR AI·2026
Same journal

Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review.

JMIR AI·2026
Same journal

Correction: Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study.

JMIR AI·2026
Same journal

AI-Assisted Systematic Literature Review of the Economic Burden of Pneumococcal Disease: Development and Validation Study.

JMIR AI·2026
Same journal

Knowledge-Augmented Large Language Model for Multimodal Electronic Health Record-Based Risk Prediction: Development and Validation Study.

JMIR AI·2026
查看所有相关文章

相关实验视频

Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

帮助大型语言模型使用临床表格用于神经行为诊断从文本的分类:算法开发和验证.

Kaiying Lin1, Abdur Rasool2, Saimourya Surabhi3

  • 1Institute of Linguistics, Academia Sinica, Taipei, Taiwan.

JMIR AI
|October 21, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 在精神病学和行为科学中显示出有限的诊断准确性. 专门的机器学习模型的性能优于当前的LLM,这表明需要用于临床应用的先进的快速工程.

关键词:
在这里,我们可以看到AIAIAI.在法学士 (LLM) 课程中.人工智能的人工智能是人工智能.聊天机器人 聊天机器人这是分类分类的分类.大型语言模型神经学诊断 神经学诊断

更多相关视频

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

相关实验视频

Last Updated: Jan 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

科学领域:

  • 人工智能的人工智能
  • 精神病学是一个精神病学.
  • 行为科学 行为科学

背景情况:

  • 大型语言模型 (LLM) 展示了先进的能力,但它们在精神病学中的诊断实用性尚未得到充分探索.
  • 使用LLM的行为科学自动诊断需要进一步调查.

研究的目的:

  • 评估神经精神疾病 (自闭症,失语,抑郁症) 的LLM聊天机器人诊断性能.
  • 比较直接诊断与代码生成提示策略,有和没有临床尺度.
  • 与传统的机器学习分类器对比LLM的性能.

主要方法:

  • 测试了ChatGPT,Gemini和Claude模型,提供直接诊断和代码生成提示.
  • 使用了ASDBank,AphasiaBank和危险分析采访集团数据集.
  • 评估性能与结构化临床评估尺度和没有结构化的临床评估尺度,与ML基准进行比较.

主要成果:

  • 临床尺度在数据集中提供了最小的性能改进.
  • 士学位的表现不一致,通常低于现有的机器学习基准.
  • 代码生成改善了AphasiaBank的F1分数 (高达86.5%),但对其他数据集的直接诊断仍然很低.

结论:

  • 当前的LLM聊天机器人,天真地提示,在精神病诊断中表现低于专门的机器学习模型.
  • 临床评估尺度可能会提供轻微的改进,但先进的快速工程对于临床实用性至关重要.