Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866915904685604864 |
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| author | Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernandez-Diaz, Raul Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C. E. |
| author_facet | Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernandez-Diaz, Raul Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C. E. |
| contents | Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2) Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernandez-Diaz, Raul Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C. E. Artificial Intelligence Other Quantitative Biology 92 J.3 Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation. |
| title | Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2) |
| topic | Artificial Intelligence Other Quantitative Biology 92 J.3 |
| url | https://arxiv.org/abs/2505.16619 |