Computational Medicine
A definition — Draft v1
Computational Medicine is an established discipline: the use of mathematics, engineering, and computational science to understand the mechanisms, diagnosis, and treatment of disease through quantitative models of its molecular biology, physiology, and anatomy.
Johns Hopkins University's Institute for Computational Medicine, founded in 2005, has defined and carried the field for two decades. Institute of ΛάΓ works within this tradition.
One continuous line
Theory
Disease read as inference
Free-energy principle · active inference
Measurement
Non-contact, patient-side sensing
OcularEVM · Memori
Data
Longitudinal accumulation
A continuous record, owned end-to-end
Related fields
A field organized into camps
Computational Medicine borders many established fields: biophysical organ modeling and patient "digital twins," computational molecular medicine and genomics, computational anatomy, computational psychiatry, systems biology, and machine learning for health.
Each is strong in its own layer, and each has advanced largely in parallel. What we contribute is a thesis about how these pieces fit together.
Our program
A vertical integration
Institute of ΛάΓ defines its work as a vertical integration across those layers — a single line running from theory to measurement to data.
Theory layer
Disease treated in terms of the structure of biological inference, in the lineage of the free-energy principle, predictive processing, and active inference. (A full theoretical framework is under review; here we state only its direction.)
Measurement layer
Physiological information acquired patient-side, at low cost and without contact: OcularEVM (non-contact intraocular-pressure estimation) and Memori (everyday infant-health records).
What is distinctive
Close to open ground
Two commitments set the program apart. First, we carry the active-inference account of disease beyond psychiatry — its usual home — toward a cross-disease and cross-organ formulation.
Second, we own the patient-side measurement and data layer end-to-end, something academic groups rarely do. Holding both at once is close to open ground, even within the well-populated English-language field.
A note on framing
Computation as a lens, not a method
We treat the computational substrate not as a technique applied to medicine, but as the lens through which medicine is seen — disease read as inference, the body as a system that models its own causes.
The emphasis is deliberate, in the same spirit that "computer science" names a stance rather than a mere set of tools.