Abstract The emergence of decentralized science (DeSci) and the potential for its acceleration through artificial intelligence (AI) present both remarkable promise and formidable challenges for modern research. DeSci-AI platforms use blockchain-based, openly governed workflows and autonomous AI ‘swarms’ capable of literature synthesis, peer-review triage, and reputation tracking to democratize access, speed discovery, and rebuild public trust. Yet, they also introduce complex implementation barriers and governance questions. The transformative claims of DeSci must be gauged against the benchmark of conventional science. Traditional science (TradSci) is anchored in methodological rigour through hypothesis-driven design, validated instrumentation, standard operating protocols, and independent peer review. Stable institutional arrangements such as universities, funding councils, and archival journals further buttress reliability by providing clear governance and normative oversight. Nevertheless, the contemporary academic reward economy, in which publication is highly valued and grant income constitutes the currency of promotion, often favours projects with near-certain, publishable outcomes. This frequently suppresses genuine curiosity, leaving potentially transformative inquiries chronically underexplored. The scientific community must therefore embrace decentralized, data-secure alternatives while preserving valuable institutional frameworks. Successful adoption of DeSci-AI hinges on interrelated technical, cultural, regulatory, and resource-related challenges. With a focus on the healthcare field, this perspective maps those barriers and proposes integrated strategies, including token-backed reviewer remuneration, incentive pools for negative or replication studies, AI-driven misconduct auditors, and hybrid governance pilots, to chart a realistic pathway for the scientific community to leverage DeSci-AI synergies while safeguarding rigour, equity, and societal trust.
Jessri et al. (Thu,) studied this question.