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沃巴蒂 ff22ed3a8b feat(deepdata): 引入修改密码、文件预览组件与清洗算子增强，deepflow 改名为 deep-flow 
本次同步 09-12 至 09-28 的代码变更，主要内容：

新增功能
- 修改密码：后端新增 ChangePasswordRequest DTO 与UserController/UserService
  对应接口，前端新增 ChangePasswordDialog 并在 Header 接入入口
- 文件预览组件重构：file-preview 目录拆分出 VideoPreview（支持 blobUrl 直传）、
  ImagePreview、AudioPreview，index.tsx 统一调度各类型预览器
- 新增 my_ops 算子模块：markitdown_parser、mineru_pdf_parser、text_quality_evaluator
- 新增 tools/minio-file-server MinIO 文件服务

功能增强
- 数据清洗：cleaning_task_service/scheduler/routes 重构，
  data_juicer_executor 与 wrapper 增强，OperatorTable/LogsTable/TaskDetail 同步调整
- 数据标注：annotation sync.py、project.py 接口与schema 更新
- 数据采集、算子市场、首页与菜单路由相关页面调整
- DataJuicer 算子接入（useOperatorOperations、dj_ops_schema）

结构调整
- deepflow/ 更名为 deep-flow/（419 个文件重命名）
- 新增 helm 部署编排、GitHub Actions 之外的自建镜像构建脚本

未纳入本次提交
- scripts-backup/、runtime/python-executor-backup/、components_backup/（备份目录）
- *.backup*、__pycache__ 产物
- zh_core_web_sm-3.8.0-py3-none-any.whl（46MB 构建产物）
- DataX/（内嵌独立仓库，远程亦未收录其源码）
2026-10-03 22:44:56 +08:00

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AI Adoption in Healthcare: Influencing Factors

Key Points

  • AI technologies like machine learning, deep learning, and NLP are rapidly changing healthcare, offering enhanced accuracy and efficiency.
  • Data quality, including volume, type, bias, security, and privacy, significantly impacts the reliability and ethical implications of AI applications in healthcare.
  • Ethical considerations, such as data privacy, algorithmic bias, and transparency, are critical for ensuring fair and equitable AI outcomes in healthcare.
  • Economic evaluations of AI in healthcare need to be comprehensive, considering initial investments, running costs, and comparisons with traditional methods.
  • Organizational readiness, including digital skills, structural adaptations, and addressing ethical concerns, is essential for successful AI integration in healthcare.
  • Healthcare lags behind other industries in AI adoption, necessitating enhanced digital infrastructure and a shift in how healthcare is delivered and accessed.

Overview

Artificial Intelligence (AI) is poised to revolutionize healthcare through machine learning, deep learning, and natural language processing. The successful integration of AI in healthcare depends on several factors, including technological maturity, data quality, ethical considerations, economic feasibility, organizational readiness, and digital infrastructure. Addressing these elements is essential for creating trustworthy and effective AI solutions that improve patient outcomes and optimize healthcare delivery.

Detailed Analysis

Technical Maturity and Validation

AI technologies, particularly machine learning (ML), deep learning (DL), and natural language processing (NLP), are increasingly prevalent in healthcare. Large Language Models (LLMs) leverage deep learning and large datasets to process text-based content. However, the accuracy, reliability, and performance of AI algorithms must be comprehensively tested using diverse datasets to avoid overfitting and ensure proper validation [https://pmc.ncbi.nlm.nih.gov/articles/PMC11047988/].

Data Availability and Quality

Data quality is crucial for the trustworthiness of AI in healthcare [https://www.nature.com/articles/s41746-024-01196-4]. Key considerations include:

  • Data Volume: AI applications require large datasets to train effectively.
  • Data Type: AI must handle both structured and unstructured data, including text, images, and sensor readings.
  • Data Bias: Biases in training data can lead to unfair or inaccurate outcomes, raising ethical concerns [https://pmc.ncbi.nlm.nih.gov/articles/PMC10718098/].
  • Data Security and Privacy: Protecting patient data is paramount, especially with increased data volumes. De-identification may not completely eliminate the risk of data linkage [https://pmc.ncbi.nlm.nih.gov/articles/PMC10718098/].

Sharing inclusive AI algorithms and retraining existing algorithms with local data can address the lack of diversity in openly shared datasets, while preserving patient privacy [https://pmc.ncbi.nlm.nih.gov/articles/PMC8515002/].

Ethical Considerations

Ethical considerations are paramount in the use of AI in healthcare [https://pmc.ncbi.nlm.nih.gov/articles/PMC11249277/]. Key issues include:

  • Privacy and Data Security: Ensuring the confidentiality and security of patient data.
  • Algorithmic Bias: Mitigating biases in algorithms to ensure equitable outcomes.
  • Transparency: Making AI decision-making processes understandable.
  • Clinical Validation: Ensuring AI tools are rigorously tested and validated for clinical use.
  • Professional Responsibility: Defining the roles and responsibilities of healthcare professionals when using AI.

Economic Costs and Benefits

Comprehensive cost-benefit analyses of AI in healthcare are needed [https://www.jmir.org/2020/2/e16866/]. These analyses should include:

Organizational Impact

AI integration impacts healthcare organizations by:

The AI application management model (AIAMA) can help manage AI implementation from an organizational perspective [https://www.sciencedirect.com/science/article/pii/S0268401223001093].

Digital Readiness

Healthcare's digital transformation through AI depends on:

Key Citations

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