Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 108 references that are in the map. First posted on arXiv 22 May 2025.
Builds on
19 of 108 references.
References · 19
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
- 2018Model Cards for Model Reportingcited by 1,068
- 2019Green AIcited by 620
- 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
- 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
- 2024Croissant: A Metadata Format for ML-Ready Datasetscited by 79
- 2019Understanding artificial intelligence ethics and safetycited by 68
- 2023FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcarecited by 59
- 2024Trust in AI: Progress, Challenges, and Future Directionscited by 35
- 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
7 more
- 2024The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligencecited by 21
- 2019QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learningcited by 19
- 2022FAIR for AI: An interdisciplinary and international community building perspectivecited by 13
- 2024What is Reproducibility in Artificial Intelligence and Machine Learning Research?cited by 11
- 2022FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopycited by 10
- 2023Recording provenance of workflow runs with RO-Cratecited by 8
- 2022Responsible Artificial Intelligence -- from Principles to Practicecited by 6
Built on by
Citing papers per year: 2026: 1.
1 paper.