Search

In the evolving landscape of artificial intelligence integration within healthcare systems, the challenge of ensuring verifiable and trustworthy clinical text generation persists, particularly in retrieval-augmented summarization pipelines. This conceptual manuscript introduces the evidence-line attribution grounding (ELAG) framework as a novel standard for anchoring generated clinical summaries to source evidence, thereby enhancing transparency and accountability in AI-driven healthcare analytics. Grounded in theoretical principles of information retrieval and attribution mechanics, ELAG delineates a multi-layered architecture that orchestrates evidence tracing across clinical data modalities, from electronic health records (EHRs) to diagnostic reports, while mitigating risks of hallucination and bias propagation in summarization outputs. We synthesize recent literature on clinical AI architectures, interoperability frameworks, and governance models to underscore the necessity for such grounding standards. The framework incorporates interpretive formulas for assessing attribution fidelity, decision confidence in clinical workflows, and governance overhead in deployment environments. By focusing on theoretical infrastructures rather than empirical validations, this work posits ELAG as a foundational blueprint for interoperable, ethical AI systems in healthcare, fostering improved clinical decision support through verifiable text generation. Ultimately, ELAG addresses critical gaps in current retrieval-augmented approaches, promoting safer integration into high-stakes clinical settings where evidence attribution directly impacts patient outcomes and regulatory compliance.