ArticleLearning health systems2026
A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge.
Article in Learning health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge.Learning health systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Persistent knowledge is essential for propagating the learning health system (LHS) cycle. Integral to the cycle are iterative transformations of data into knowledge. However, human efforts to undertake these transformations are increasingly challenged when dealing with larger data scales and complexities. Data sets within repositories and archives are often underutilized unless specifically requested for research programs. Specialized software algorithms (agents) can use existing knowledge for learning tasks, explore their environment, discover and create goals, and interact with humans. Methods: This paper examines the potential role of software intelligence for autonomous transformations of data and knowledge. Agents can perform various goal-directed tasks. Multi-agent systems can be utilized for data collection, description, preparation, modeling, and knowledge-mining tasks. Knowledge representation, ontologies, semantic web standards, knowledge bases, and graphs can lead to a higher level of directed learning. Agents can develop reasoning abilities and self-generate goals by leveraging semantic relationships between various datasets. Results: A conceptual framework for an intelligent biomedical platform (IBP) is proposed. The IBP comprises four layers: infrastructure (IS), user interface (UI), coordination system (CS), and data and knowledge (DK). It also integrates a network of multi-agent systems for clinical decision-making and knowledge-mining tasks. Intelligence in the platform results from the interaction of the IS, UI, CS, and DK agents. These agents can implement multiple inferential steps using the data and knowledge within accessible repositories. Large language models can be integrated with various knowledge resources and domain-specific databases, thereby improving the accuracy of results. Conclusion: An IBP supported by a multi-agent system can enhance the autonomous transformation of data and knowledge. Including software intelligence within current repositories and archives enhances data reuse and the generation of new knowledge. With the addition of software reasoning capabilities in biomedical platforms, the LHS cycle can be efficiently propagated to aid in newer biomedical discoveries.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.