AI-Driven Clinical Reporting: A Case Study on IQLINIQ

Leveraging the power of generative artificial intelligence (AI) may assist the clinicians in providing prompt care, as well as reducing the financial pressure on patients. Although current generative AI technologies present a possible route for automation, they often lack in report accuracy, quality, and addressing privacy concerns. This paper presents a novel framework for the automatic creation of high-quality reports from clinical meeting audio transcripts. This framework is a part of our clinic management platform (IQLINIQ) and benefits from a three-stage process, including precise audio-to-text transcription, internal anonymization, and a refining phase to ensure consistency and conformity to clinical standards. Using both quantitative measures, including cost and time analysis, and qualitative evaluations, we compare the AI-driven reports against expert-generated ones, individual large language models (LLMs), and a state-of-the-art baseline model, GPT-4-o. Our findings show that our framework noticeably enhances the report preparation process by significantly reducing the time and cost of generating a report from expert-surveyed 3 hours and 750 US-dollars for a complete report to less than 5 minutes and 1 US-dollar, respectively. Improving the report quality by over 10 points compared to existing techniques also underlines the effectiveness of proposed solution.