The Latent Space Hypothesis: Toward Universal Medical Representation Learning

Authors: Salil Patel

arXiv: 2506.04515v1 - DOI (q-bio.QM)
51 pages, 12 figures. A position paper examining the latent space hypothesis - the proposition that diverse medical data can be represented in shared latent spaces reflecting fundamental biological processes. The paper discusses theoretical foundations, reviews supporting evidence, and considers potential implications for medical AI and representation learning
License: CC BY 4.0

Abstract: Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives. Although these modalities appear disparate, many encode convergent information about a single underlying physiological state. The Latent Space Hypothesis frames each observation as a projection of a unified, hierarchically organized manifold -- much like shadows cast by the same three-dimensional object. Within this learned geometric representation, an individual's health status occupies a point, disease progression traces a trajectory, and therapeutic intervention corresponds to a directed vector. Interpreting heterogeneous evidence in a shared space provides a principled way to re-examine eponymous conditions -- such as Parkinson's or Crohn's -- that often mask multiple pathophysiological entities and involve broader anatomical domains than once believed. By revealing sub-trajectories and patient-specific directions of change, the framework supplies a quantitative rationale for personalised diagnosis, longitudinal monitoring, and tailored treatment, moving clinical practice away from grouping by potentially misleading labels toward navigation of each person's unique trajectory. Challenges remain -- bias amplification, data scarcity for rare disorders, privacy, and the correlation-causation divide -- but scale-aware encoders, continual learning on longitudinal data streams, and perturbation-based validation offer plausible paths forward.

Submitted to arXiv on 04 Jun. 2025

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