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<p>Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.</p>

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Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Gavin Farrell,
  • Eleni Adamidi,
  • Rafael Andrade Buono,
  • Mihail Anton,
  • Omar Abdelghani Attafi,
  • Salvador Capella Gutierrez,
  • Emidio Capriotti,
  • Leyla Jael Castro,
  • Davide Cirillo,
  • Lisa Crossman,
  • Christophe Dessimoz,
  • Alexandros Dimopoulos,
  • Raúl Fernández-Díaz,
  • Styliani-Christina Fragkouli,
  • Carole Goble,
  • Wei Gu,
  • John M. Hancock,
  • Alireza Khanteymoori,
  • Tom Lenaerts,
  • Fabio G. Liberante,
  • Peter Maccallum,
  • Alexander Miguel Monzon,
  • Magnus Palmblad,
  • Lucy Poveda,
  • Ovidiu Radulescu,
  • Denis C. Shields,
  • Shoaib Sufi,
  • Thanasis Vergoulis,
  • Fotis Psomopoulos,
  • Silvio C. E. Tosatto

摘要

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.