Challenges of reproducible AI in biomedical data science
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Genomics |
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| Online Access: | https://doi.org/10.1186/s12920-024-02072-6 |
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| _version_ | 1841544194592604160 |
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| author | Henry Han |
| author_facet | Henry Han |
| author_sort | Henry Han |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-539401db4d244dd19ec2702bbe8b3686 |
| institution | Kabale University |
| issn | 1755-8794 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj-art-539401db4d244dd19ec2702bbe8b36862025-01-12T12:43:39ZengBMCBMC Medical Genomics1755-87942025-01-0118S11710.1186/s12920-024-02072-6Challenges of reproducible AI in biomedical data scienceHenry Han0The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor UniversityAbstract 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.https://doi.org/10.1186/s12920-024-02072-6AIReproducibilityBiomedical dataGame-theory |
| spellingShingle | Henry Han Challenges of reproducible AI in biomedical data science BMC Medical Genomics AI Reproducibility Biomedical data Game-theory |
| title | Challenges of reproducible AI in biomedical data science |
| title_full | Challenges of reproducible AI in biomedical data science |
| title_fullStr | Challenges of reproducible AI in biomedical data science |
| title_full_unstemmed | Challenges of reproducible AI in biomedical data science |
| title_short | Challenges of reproducible AI in biomedical data science |
| title_sort | challenges of reproducible ai in biomedical data science |
| topic | AI Reproducibility Biomedical data Game-theory |
| url | https://doi.org/10.1186/s12920-024-02072-6 |
| work_keys_str_mv | AT henryhan challengesofreproducibleaiinbiomedicaldatascience |