Scholarlink

Background <p>Artificial intelligence (AI) has become a transformative force in precision medicine, offering unprecedented capabilities in data integration, predictive modeling, and decision support. The convergence of AI with clinical decision support systems (CDSS) and intelligent care pathways (ICPs) holds potential to revolutionize individualized patient care. However, evidence on their real-world implementation, translational challenges, and ethical implications remains fragmented.</p> Objective <p>This narrative review aimed to synthesize current literature on AI-integrated clinical decision systems and intelligent care pathways within the context of precision medicine, focusing on their applications, implementation barriers, and clinical impact across healthcare domains.</p> Methods <p>A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for studies published up to December 2025. Eligible works included peer-reviewed empirical studies, reviews, conceptual frameworks, and high-impact case studies addressing AI-CDSS or ICPs in precision medicine. Thematic synthesis was employed to identify patterns in applications, infrastructural needs, ethical considerations, and translational challenges. Findings were organized into thematic domains and critically appraised using a structured quality-assessment rubric.</p> Results <p>From approximately 350 records, 150 publications met inclusion criteria. Evidence strength varied considerably: 14.7% high, 36.0% moderate, and 49.3% low. AI-CDSS demonstrated enhanced diagnostic precision, treatment optimization, and workflow efficiency across oncology, cardiology, and neurology. Multimodal data integration, explainable AI (XAI), and federated learning emerged as key enablers of clinical adoption. Real-world implementations showed measurable impact, while others like IBM Watson for Oncology revealed persistent gaps in concordance and outcome validation. Implementation challenges remain pervasive. Critical evidence gaps persist in longitudinal outcome validation, cross-institutional generalizability, and equitable performance across diverse populations. Education, transparent model governance, and clinician co-design emerged as critical facilitators for sustainable integration.</p> Conclusions <p>AI-integrated clinical decision systems and intelligent care pathways represent a paradigm shift toward precision, personalization, and data-driven healthcare. While promising, their successful translation depends on harmonized data ecosystems, ethical AI governance, robust multi-center validation, and sustained clinician engagement. This synthesis underscores the urgent need for interdisciplinary collaboration, standardized validation protocols (e.g., TRIPOD-AI, PROBAST-AI), and adaptive policy frameworks to bridge the gap between algorithmic potential and equitable clinical reality.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-integrated clinical decision support systems for precision medicine in real-world healthcare

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

查看全文
  • Aliasghar Khakpaki,
  • Sajjad Aghasi Javid,
  • Azizeh Farshbaf-Khalili,
  • Hassan Sepehri

摘要

Background

Artificial intelligence (AI) has become a transformative force in precision medicine, offering unprecedented capabilities in data integration, predictive modeling, and decision support. The convergence of AI with clinical decision support systems (CDSS) and intelligent care pathways (ICPs) holds potential to revolutionize individualized patient care. However, evidence on their real-world implementation, translational challenges, and ethical implications remains fragmented.

Objective

This narrative review aimed to synthesize current literature on AI-integrated clinical decision systems and intelligent care pathways within the context of precision medicine, focusing on their applications, implementation barriers, and clinical impact across healthcare domains.

Methods

A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for studies published up to December 2025. Eligible works included peer-reviewed empirical studies, reviews, conceptual frameworks, and high-impact case studies addressing AI-CDSS or ICPs in precision medicine. Thematic synthesis was employed to identify patterns in applications, infrastructural needs, ethical considerations, and translational challenges. Findings were organized into thematic domains and critically appraised using a structured quality-assessment rubric.

Results

From approximately 350 records, 150 publications met inclusion criteria. Evidence strength varied considerably: 14.7% high, 36.0% moderate, and 49.3% low. AI-CDSS demonstrated enhanced diagnostic precision, treatment optimization, and workflow efficiency across oncology, cardiology, and neurology. Multimodal data integration, explainable AI (XAI), and federated learning emerged as key enablers of clinical adoption. Real-world implementations showed measurable impact, while others like IBM Watson for Oncology revealed persistent gaps in concordance and outcome validation. Implementation challenges remain pervasive. Critical evidence gaps persist in longitudinal outcome validation, cross-institutional generalizability, and equitable performance across diverse populations. Education, transparent model governance, and clinician co-design emerged as critical facilitators for sustainable integration.

Conclusions

AI-integrated clinical decision systems and intelligent care pathways represent a paradigm shift toward precision, personalization, and data-driven healthcare. While promising, their successful translation depends on harmonized data ecosystems, ethical AI governance, robust multi-center validation, and sustained clinician engagement. This synthesis underscores the urgent need for interdisciplinary collaboration, standardized validation protocols (e.g., TRIPOD-AI, PROBAST-AI), and adaptive policy frameworks to bridge the gap between algorithmic potential and equitable clinical reality.