GENERATIVE AI IN HEALTHCARE: AUTOMATING CLINICAL DOCUMENTATION, DIAGNOSTICS, AND KNOWLEDGE SYNTHESIS
DOI:
https://doi.org/10.15680/kqrsrr03Keywords:
Generative AI, Large Language Models, Clinical Documentation, Radiology Report Generation, Knowledge Synthesis, Responsible AI, Healthcare Automation, AI Governance, Bias Mitigation, Explainable AIAbstract
The integration of Generative Artificial Intelligence (GenAI) into healthcare is
transforming how clinical information is captured, interpreted, and applied. By
leveraging large language models (LLMs) and multimodal architectures, GenAI
enables automation in medical documentation, diagnostic support, and knowledge
synthesis—reducing clinician workload and improving decision quality. This paper
presents an in-depth exploration of responsible GenAI deployment across three key
domains: (1) clinical documentation automation, where AI-driven transcription and
summarization minimize administrative burden; (2) radiology report generation,
combining image understanding with natural language generation for faster, consistent
diagnostics; and (3) decision summarization and medical knowledge synthesis, which
distills evidence-based insights to support clinical judgment. Using a combination of
recent empirical studies, benchmark evaluations, and regulatory frameworks, we
examine accuracy, bias, and safety implications of LLM-based systems in real-world
healthcare settings. The study emphasizes ethical guardrails—privacy preservation,
auditability, and explainability—as prerequisites for trustworthy GenAI adoption. The transparency, and continuous post-deployment evaluation to ensure clinical reliability and compliance with evolving AI-in-healthcare regulations.
findings advocate for a balanced framework integrating human oversight, algorithmic
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