The Latent Space Hypothesis: Toward Universal Medical Representation Learning

• URL: http://arxiv.org/abs/2506.04515v1
• Date: Wed, 04 Jun 2025 23:37:33 GMT
• Title: The Latent Space Hypothesis: Toward Universal Medical Representation Learning
• Authors: Salil Patel,
• Abstract summary: Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives.<n>The Latent Space Hypothesis frames each observation as a projection of a unified, hierarchically organized manifold.<n>By revealing sub-trajectories and patient-specific directions of change, the framework supplies a quantitative rationale for personalised diagnosis.
• Score: 0.0
• License: http://creativecommons.org/licenses/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.

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