Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation
arXiv:2512.09185v4 Announce Type: replace-cross Abstract: Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity with random denoising process. In this work, we propose to treat the disease dynamic as a velocity field and leverage Flow Match
This research addresses fundamental limitations in existing generative models for disease progression, indicating a maturation in AI's application to complex biological dynamics.
Improved patient-specific disease dynamics can lead to more accurate early diagnoses and highly individualized treatment plans across various medical conditions.
The ability to model disease progression as continuous velocity fields, rather than discrete steps, offers a more biologically plausible and potentially more effective approach.
- · Biopharmaceutical companies
- · Medical diagnostic firms
- · Healthcare providers
- · AI research institutions
- · Traditional statistical modeling approaches for disease progression
- · Generative models lacking continuous latent representations
More precise medical imaging analysis and predictive diagnostics will become feasible.
Personalized medicine initiatives will accelerate, driven by higher fidelity disease progression models.
The ethical and regulatory frameworks around AI-driven diagnosis and treatment recommendations will require significant development.
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Read at arXiv cs.AI