Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences
🤖 AI Summary
Facing trust deficits, irreproducibility, model non-reusability, and ecosystem fragmentation in AI applications for life sciences, this study systematically catalogs over 300 AI ecosystem components—the first such comprehensive mapping—and introduces the Mapping-based Open Sustainable AI (OSAI) practice framework. Methodologically, it integrates interdisciplinary literature review, ecosystem component mapping, community-driven consensus building, and sustainable framework design, while aligning with international initiatives including FAIR, TRUST, and LEADR. The primary contribution is a pragmatic, actionable OSAI recommendation set that substantially enhances AI model transparency, reproducibility, reusability, and environmental sustainability. This framework provides policymakers and practitioners with a structured pathway for implementation, thereby enabling scalable, standardized, and responsible AI deployment across life science domains.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Addressing poor reusability and reproducibility in AI research outputs
Fragmented AI ecosystem lacks guiding pathways for sustainable development
Connecting researchers with resources for transparent and sustainable AI implementation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces Open and Sustainable AI (OSAI) recommendations
Maps OSAI to over 300 AI ecosystem components
Facilitates sustainable, reusable, transparent AI implementation
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