Enhancing Endoscopy Report Quality Through Next‐Generation <scp>AI</scp> : Complementing Current Systems With Generative Models, Advanced Speech Recognition, and Robust Natural Language Processing

Abstract

We read with great interest the comprehensive review by Sekiguchi et al., “Artificial Intelligence and Its Impact on the Quality of Endoscopy Reports” [1]. Their systematic examination of landmark recognition systems, cecum identification algorithms, and natural language processing (NLP) applications in the J-SCOUT study (89.3% success rate structuring 86,049 pathology reports) demonstrates significant strides in reducing endoscopist workload. However, we propose three complementary enhancements to advance this field further. First, generative AI models (GPT-4, Claude) offer transformative report generation possibilities. Unlike template-based systems, large language models (LLMs) trained on medical corpora can generate coherent, narrative-style reports reflecting clinical reasoning [2]. Zaretsky et al. demonstrated LLM-generated discharge summaries achieved 54% conversion rates with improved readability (conversion means that transforms hospital discharge summaries into a format that is readable for patients). These models can automatically generate patient-friendly versions, translating “adenomatous polyp” to “a growth that might develop into cancer if left untreated.” We propose LLM-generated drafts with mandatory endoscopist review. Second, voice recognition systems need refinement for real-world challenges: background noise, medical terminology variations, multiple speakers, and workflow integration [3]. Advanced systems should incorporate endoscopy suite-specific noise-cancellation, adaptive medical dictionaries, speaker diarization, and multilingual support. Third, NLP should expand beyond pathology-endoscopy linkage to real-time quality monitoring, guideline compliance checking, and predictive analytics [4]. This requires greater clinical engagement and open data sharing. Across all three applications, implementation must prioritize safety and privacy. In medical LLM applications, these considerations are particularly critical: LLMs handle sensitive patient data (e.g., medical histories), whose leakage risks serious consequences like identity theft or legal violations; meanwhile, LLM “hallucinations” could mislead clinical decisions and threaten patient safety. LLM “hallucinations” necessitate robust validation, mandatory human oversight, and continuous monitoring. Secure deployments and GDPR/HIPAA compliance are paramount [5]. Enjian Liu: formal analysis, writing – original draft, writing – review and editing. Zekai Yu: investigation, formal analysis, writing – review and editing. The author has read and agreed to the published version of the manuscript. The authors have nothing to report. The authors have nothing to report. The authors have nothing to report. The authors have nothing to report. The authors declare no conflicts of interest.

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