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I enthusiastically read the article “Usefulness of Automated Analysis System for Microvascular Blood Flow Rate Using Magnifying Endoscopy with Blue Laser Imaging to Differentiate Early Gastric Cancer from Patchy Redness” by Akazawa et al., published in the Journal of Gastroenterology and Hepatology (2025) [1]. The authors deserve appreciation for addressing an innovative and clinically relevant problem, differentiating early gastric cancer from benign mucosal changes using an automated analytical system. Their effort to integrate objective microvascular flow quantification into endoscopic diagnostics reflects a meaningful step toward enhancing diagnostic accuracy and reducing observer variability in gastrointestinal oncology. Such technology-driven approaches hold great promise in bridging the gap between expert-level endoscopy and practical clinical application, particularly in early cancer detection where visual ambiguity often challenges even experienced clinicians. I would like to draw your attention to a perspective that the study has missed. Firstly, the algorithm should undergo rigorous external, multicenter validation using independent datasets to eliminate overfitting and confirm reproducibility across different equipment, operators, and populations [2]. This paper stresses that AI systems trained and tested on the same institutional data often show inflated accuracy and fail to replicate across new datasets. It reinforces your point that Akazawa et al.'s algorithm lacks external validation and may not generalize to different endoscopy units. [3] warn that models tested only within one dataset are prone to “optimistic bias.” Reinforces that single-center validation is insufficient for clinical reliability. Secondly, the inclusion criteria must be broadened to encompass the full pathological and technical spectrum of gastric lesions including suboptimal videos and invisible microvessels to avoid spectrum bias and more accurately reflect clinical reality [4]. Lijmer et al. demonstrated that design flaws such as case–control sampling systematically inflate diagnostic accuracy. Their findings validate the claim that the study's selective inclusion distorts its results. [5] explain that excluding indeterminate or difficult cases creates spectrum bias, falsely inflating accuracy matching Akazawa et al.'s exclusion of invisible vessels. Thirdly, comprehensive data on physiological and pharmacologic confounders such as sedation depth, hemodynamic status, and medication use should be collected and statistically adjusted for, ensuring that observed flow-rate variations truly reflect pathology rather than systemic factors [6]. This paper highlights wide physiologic variability under sedation and the consequent impact on ventilation and perfusion. It confirms the point that interpatient differences can confound flow-rate readings if unaccounted for. [7] explains that hidden confounders cause biased AI models and shows why clinical variables must be controlled for credible predictive outcomes. Fourthly, algorithmic transparency must be enhanced by disclosing its core computational framework or pseudocode and enabling independent replication by neutral researchers, minimizing bias linked to proprietary control or funding sources [8]. Fehr et al. empirically evaluated commercial medical-AI tools and found most lacked documentation of training data, demographics, and monitoring. This demonstrates how proprietary systems, like the one critiqued, impede reproducibility and safety evaluation. [9] conclude that reproducibility demands open-source code and third-party testing, both absent in the current study. Finally, a larger, prospectively powered cohort with standardized imaging protocols and stratified subgroup analysis is vital to yield stable, generalizable diagnostic thresholds. Implementing these measures would convert this prototype from an interesting pilot into a clinically deployable diagnostic aid [10]. This study proved that small, poorly powered diagnostic studies tend to report exaggerated accuracy. It validates the critique that 76 videos cannot yield stable or generalizable cut-offs. [11] quantitatively prove that small or correlated datasets lead to false model performance, mirroring Akazawa's limited video sample. While the study by Akazawa et al. represents a noteworthy step toward integrating automated vascular-flow analytics into endoscopic cancer detection, its interpretive strength is undermined by methodological and transparency limitations. Without rigorous external validation, comprehensive control of physiological confounders, and full algorithmic disclosure, the diagnostic performance observed remains contextually fragile. True clinical translation of such systems demands not only technical innovation but also methodological integrity, multicenter reproducibility, interpretability, and ethical transparency. Until these foundations are established, automated endoscopic AI will remain an intriguing prototype rather than a dependable clinical tool capable of transforming early gastric cancer diagnostics model performance, mirroring Akazawa's limited video sample. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.