Han Henry
The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX, 76798, USA.
BMC Med Genomics. 2025 Jan 10;18(Suppl 1):8. doi: 10.1186/s12920-024-02072-6.
Artificial intelligence (AI) is revolutionizing biomedical data science at an unprecedented pace, transforming various aspects of the field with remarkable speed and depth. However, a critical issue remains unclear: how reproducible are the AI models and systems employed in biomedical data science? In this study, we examine the challenges of AI reproducibility by analyzing the factors influenced by data, model, and learning complexities, as well as through a game-theoretical perspective. While adherence to reproducibility standards is essential for the long-term advancement of AI, the conflict between following these standards and aligning with researchers' personal goals remains a significant hurdle in achieving AI reproducibility.
人工智能（AI）正以前所未有的速度彻底改变生物医学数据科学，以前所未有的速度和深度改变该领域的各个方面。然而，一个关键问题仍不明确：生物医学数据科学中使用的人工智能模型和系统的可重复性如何？在本研究中，我们通过分析受数据、模型和学习复杂性影响的因素，并从博弈论的角度来审视人工智能可重复性面临的挑战。虽然遵守可重复性标准对人工智能的长期发展至关重要，但遵循这些标准与符合研究人员个人目标之间的冲突仍然是实现人工智能可重复性的一个重大障碍。
