Computational Molecular Biology 2025, Vol.15, No.4, 208-217 http://bioscipublisher.com/index.php/cmb 21 5 surveys show that standardized reports make it easier for them to understand the test results. Some patients who have received popular science education can even interpret the significance of driver mutations by themselves. Overall, the benefits brought by this standardized process are multi-faceted. Firstly, it ensures that key variations will not be missed, guaranteeing the integrity of the diagnosis. Secondly, a unified report template reduces the differences among analysts, making the results more consistent and reliable. Thirdly, automation has significantly shortened the analysis cycle-the average time from submission for inspection to reporting has been reduced from 10 working days to 7. This improvement is particularly important for patients with advanced tumors who need to make quick decisions. The doctor satisfaction survey also shows that after using the new process, their trust in the test results and understanding of the reports have both improved. Nevertheless, there are still areas that need improvement. For instance, there are still certain limitations in the detection of low-frequency variations (<5 %). In the future, it is planned to introduce UMI markers or adopt more sensitive algorithms. Meanwhile, for the classification of rare mutations, they are currently mostly treated as “unclear meaning”, and the interpretation may be further optimised in combination with AI-assisted annotation systems. 7 Challenges and Future Prospects Artificial intelligence (AI) has been increasingly prominent in genomic data analysis in recent years, especially in machine learning and deep learning technologies. Many of the analysis steps that originally relied on manual rule setting and parameter adjustment can now be "learned" and completed by the algorithms themselves. Take mutation detection as an example. AI models are beginning to replace traditional statistical methods. For instance, Google's DeepVariant has significantly improved the accuracy of mutation recognition by leveraging deep neural networks. It can be imagined that in the future, standardized processes are likely to be equipped with such an AI Caller, no longer merely performing command-based tests, but further reducing false positives while ensuring sensitivity. Such integration makes standardized processes smarter, moving from automation to true intelligence. However, AI is not omnipotent. Issues such as the interpretability of the model, data bias, and transparency remain thorny. Even if a model performs extremely well, if it cannot explain why a certain conclusion is given, it is still difficult to be fully trusted in clinical scenarios. Therefore, in the future, standardization organizations may need to introduce corresponding norms to ensure that AI models must undergo strict validation and clearly indicate the confidence range when outputting results. From a broader perspective, the integration of AI is merely one direction in the evolution of bioinformatics process standardization. The real challenge lies in how to keep the process at an "updated pace" in the rapidly changing technological environment. With the continuous evolution of sequencing technology, new platforms such as long-read and single-molecule sequencing have gradually entered clinical practice, and new algorithms are also emerging one after another. If standardized processes cannot be dynamically adjusted, they will soon be left behind by The Times. Therefore, it is crucial to establish a mechanism that can be regularly reviewed, evaluated and improved. Just as the medical field often refers to "continuous quality improvement", processes also require this kind of cyclical iteration - planning (Plan), doing (Do), checking (Check), and acting (Act), constantly making corrections and optimizations. In the future, perhaps we will witness the emergence of regional or even national genomic data platforms. The variant and phenotypic data produced by different hospitals through a unified process are aggregated and shared, achieving the true meaning of "one process, national reference". Of course, beyond technology, there are also complex issues such as data mutual recognition and legal compliance, but standardized output is undoubtedly the first step taken. Overall, the integration of AI and standardization is unstoppable. It will enable bioinformatics processes not only to "run automatically" but also to "learn to run", ultimately making clinical genomics analysis more efficient and accurate. Acknowledgments The authors extend sincere thanks to two anonymous peer reviewers for their invaluable feedback on the manuscript.
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