While the rise of preprint servers has commendably solved some problems of immediate dissemination, the formal validation and publication process remains a significant bottleneck [6]. The subsequent journey from a preprint to a peer-reviewed, recognized publication still involves an agonizing delay, often extending from 12 to 18 months [5]. For digital health, where technology can become obsolete in a single year, this means the evidence base is consistently lagging behind the innovation curve.

This obsolescent system is governed by an economic model that creates profound access and equity issues [7]. Top-tier research universities report annual subscription expenditures exceeding US $10 to $15 million [8], while author-facing article processing charges in prestigious journals can range from US $5000 to over $11,000 per article [9], creating an unsustainable dual financial burden.

Beyond speed and cost, the very foundation of our scientific evidence faces new and ongoing threats. The scholarly record is plagued by growing research integrity issues, with an exponential rise in issues from gift authorship and peer-review rings to outright data fabrication from organized paper mills [10]. This is compounded by the well-documented reproducibility crisis, where it is estimated that a significant percentage of published research findings are not reproducible, with the proportions ranging widely from 50% to 90% by different disciplines and measures [11,12].

In my view, a core constraint underlying these issues is the system’s primary output: the static, text-based article. This opaque narrative summary functionally decouples an author’s claims from the underlying data and analytical methods, making verification challenging. For digital health, the stakes are particularly high. The black box of a clinical AI model cannot be built on the black box of a nonreproducible study.

In response to this crisis, a chaotic ecosystem of AI tools has emerged. While many general-purpose large language models already have deep research functionality, they represent a patchwork approach that ultimately adds complexity without addressing systemic failures.

The solution is not to incrementally speed up the old system or add more disconnected tools. We need a fundamental reimagining of how we create, validate, and share scientific knowledge. We need a new operating system for science that is dynamic, transparent, data driven, and prespecified in research protocols, unified coherently on an open platform, and powered by AI technologies with endorsement from humans, who are responsible for its outputs.

This new model must move beyond the static paper as its primary output. The future unit of publication must be a living, verifiable, and interactive record as an enriched dynamic research object, where the data, methods, analysis log, fair author contributions, and transparent peer validation are all structurally and permanently linked, captured, and time-stamped.

This single integrated ecosystem—where AI is not a collection of fragmented patches but the core engine of one workflow that ensures rigorous reporting and transparency by design—is imperative for the integrity of science and fidelity of academic communication. The digital health community is at the forefront of innovation. We have a unique responsibility and opportunity to lead the charge in building such an AI-powered publishing model and scientific evidence ecosystem that is aligned with open science principles [19] and worthy of an immediate presence and the future [20]. The technology is (almost) here [21,22]. What is required now is the collective will to build, adopt, and apply it.