The Evolution of Clinical Intelligence Through GenAI Co-pilots: A Systematic Review and Thematic Synthesis
TL;DR
A conceptual Clinical Co-pilot Framework is proposed to position GenAI as a collaborative partner that supports clinicians rather than replaces them, which provides a conceptual basis for future empirical validation and may help inform the responsible implementation of GenAI in healthcare.
Abstract
Generative artificial intelligence (GenAI), large language models (LLMs), and multimodal foundation models are rapidly transforming healthcare by extending artificial intelligence beyond traditional predictive analytics toward collaborative clinical intelligence. Recent advances have enabled applications in clinical decision support, medical documentation, workflow optimization, patient communication, and personalized care planning. However, the existing literature remains fragmented across technical evaluations, specialty-specific applications, and governance discussions, resulting in a limited understanding of how these technologies collectively function within clinical environments. This study conducted a systematic review and thematic synthesis to examine the emerging role of GenAI as a clinical co-pilot in healthcare. Following the PRISMA 2020 framework, literature was retrieved from Web of Science, PubMed, and Scopus databases. A total of 3,124 records were identified, and 41 studies published between 2023 and March 2026 met the eligibility criteria and were included in the final qualitative synthesis. Quality assessment indicated that 87.8% of the included studies were classified as moderate or high quality, providing a robust methodological basis for the thematic synthesis. Thematic analysis revealed three interrelated domains underlying the evolution of GenAI-enabled clinical intelligence: 1) Perception and Fact Anchoring, involving multimodal data integration, retrieval-augmented generation (RAG), and domain-specific medical intelligence; 2) Clinical Agency and Collaboration, encompassing ambient digital scribing, agentic clinical decision support, workflow augmentation, and personalized care; and 3) Governance and Responsibility, including human-in-the-loop oversight, privacy protection, regulatory compliance, fairness, and trustworthy AI practices. Based on these findings, a conceptual Clinical Co-pilot Framework is proposed to position GenAI as a collaborative partner that supports clinicians rather than replaces them. The framework provides a conceptual basis for future empirical validation and may help inform the responsible implementation of GenAI in healthcare.