ArticleiMeta2026
Artificial intelligence models Traditional Chinese Medicine principles to integrate with biomedicine.
Article in iMeta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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0 citing papers in PubMed.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using 26,339 model-eligible Traditional Chinese Medicine (TCM) formula records, we trained an autoencoder following TCM's compression-expansion logic to learn the TCM embedding space (TCM-ES), quantifying symptom-pattern-formula mappings. TCM-ES aligns with conventional TCM principles not used in training. Biomedical entities were mapped post hoc into the fixed space, supporting formula prediction, retrospective clinical analyses, and exploratory therapeutic prioritization.
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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.