Longitudinal care
Outpatient and ward systems that turn continuous patient state into timely, accountable action.
An independent research program
Medicine, redesigned
for machines that can act.
How should clinical systems change when intelligent machines can perceive patient states, select interventions, act on patients, and learn from their consequences?
Outpatient careWard managementRehabilitationSurgeryUltrasoundCatheterization
01 / Thesis
The premise
Most medical AI predicts. The next generation will participate.
Medicine was designed around humans as the sole agents of observation, judgment, and physical intervention. That assumption is beginning to break.
This project studies the data, interfaces, safeguards, workflows, and institutions required when AI moves from describing care to taking part in it.
02 / Clinical Action Atlas
One framework.
Many forms of care.
A living map of where machines can observe, decide, intervene, and learn—with clinical risk kept visible.
Embodied intervention
Rehabilitation, imaging, catheterization, and surgery.
Learning infrastructure
Action datasets, outcome links, world models, evaluation, and governance.
Open research library03 / Field Notes
A public notebook for an emerging field.
Paper analyses, synthesis essays, datasets, research groups, and original working hypotheses.
From clinical decision support to clinical action systems
Why prediction is only the beginning.
Levels of autonomy in AI-native medicine
A working taxonomy across risk and oversight.